Haixiang Wang
email: lwang@psych.ac.cn

Aileen Mcgonigal

Kai Zhang

Qiang Guo

Bingqing Zhang

Xiu Wang

Xiao Wang

Jiuluan Lin

Xiancheng Song

Qian Feng

Siyu Wang

Mengyang Wang

Xiaoqiu Shao

Xiaoyan Liu

Liang Wang

Wenjing Zhou

Semiologic subgroups of insulo-opercular seizures based on connectional architecture atlas

Keywords: Brainnetome Atlas, cluster analysis, insular seizure, semiology, stereoelectroencephalography

Objective: Insulo-opercular seizures are characterized by diverse semiology, related to the insula's multiple functional roles and extensive connectivity. We aimed to identify semiologic subgroups and correlate these with insulo-opercular subregions based on connectional architecture. Methods: We retrospectively collected a large series of 37 patients with insulo-Funding information

opercular seizures explored by stereoelectroencephalography (SEEG) from three epilepsy centers. A new human brain atlas (Brainnetome Atlas, BNA) based on both anatomic and functional connections was employed to segment insulo-opercular cortex. Semiology and SEEG changes were carefully reviewed and quantified. Principal component analysis and cluster analysis were used to correlate semiologic characteristics with insulo-opercular subregions. Results: Four main semiologic subgroups were identified, organized along an anteroventral to posterodorsal axis based on BNA. Group 1 was characterized by epigastric sensation and/or integrated gestural motor behaviors with or without feelings of fear or rage, involving the anteroventral insular regions and mesial temporal lobes. Group 2 was characterized by auditory sensations and symmetric proximal/axial tonic signs involving the posteroventral temporal operculum. The characteristics of group 3 were orofacial and laryngeal signs, involving the intermediate insulo-opercular regions. The features of group 4 were somatosensory signs followed by nonintegrated gestural motor behaviors and/or asymmetric tonic signs involving the posterodorsal insuloopercular regions with propagation to the mesial frontal lobes. Thus anteroventral seizure organizations predominantly showed limbic system semiology, whereas more posterodorsal regions were associated with semiology involving mainly the sensorimotor system. Subjective symptoms proved to be particularly discriminating factors. Significance: Insulo-opercular seizures can be categorized in terms of clinical semiology and correlate with connectional architecture subregions along an anteroventral-posterodorsal axis in line with the cytoarchitectonic gradient rather than the gyral anatomy EPILEPSIA APRIL 2020 [START_REF] Afif | Safety and usefulness of insular depth electrodes implanted via an oblique approach in patients with epilepsy[END_REF]

| INTRODUCTION

The insular lobe is a deeply located structure covered by dense vasculature and the frontal-parietotemporal opercula. Stereoelectroencephalography (SEEG) is a well-suited approach that provides direct access by combining lateral orthogonal trajectories through opercula, with an oblique approach through frontal and/or parietal cortices. [START_REF] Afif | Safety and usefulness of insular depth electrodes implanted via an oblique approach in patients with epilepsy[END_REF] A variety of subjective and objective signs have been recorded through SEEG insular explorations, including viscerosensory, somatosensory, and auditory sensations; autonomic symptoms; gestural motor behaviors; and elementary motor signs. [START_REF] Isnard | Semiology of insular lobe seizures[END_REF][START_REF] Obaid | Insular Epilepsy: Semiology and Noninvasive Investigations[END_REF][START_REF] Isnard | The role of the insular cortex in temporal lobe epilepsy[END_REF][START_REF] Isnard | Clinical Manifestations of Insular Lobe Seizures: A Stereo-electroencephalographic Study[END_REF][START_REF] Peltola | Anatomoelectroclinical features of SEEG-confirmed pure insular-onset epilepsy[END_REF] The diversity of insular seizures reflects distinctive propagation pathways including perisylvian, temporal, and frontal patterns [START_REF] Cerliani | Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex[END_REF][START_REF] Jobst | The insula and its epilepsies[END_REF] ; as such, insular seizures are great mimickers of seizures originating from elsewhere in the brain. [START_REF] Nguyen | Revisiting the role of the insula in refractory partial epilepsy[END_REF] On the other hand, seizures arising from other lobes also often involve insulo-opercular cortex, such as temporal-plus epilepsy. [START_REF] Barba | The insula in temporal plus epilepsy[END_REF] Consequently, the diversity of clinical semiology and epileptogenic networks often leads to failure of diagnosis and surgical treatment in insulo-opercular seizures.

Insulo-opercular seizures became a subject of study and clinical interest through pioneering works using SEEG, notably from the Lyon group. [START_REF] Isnard | The role of the insular cortex in temporal lobe epilepsy[END_REF][START_REF] Isnard | Clinical Manifestations of Insular Lobe Seizures: A Stereo-electroencephalographic Study[END_REF] However, since their first descriptions, a main difficulty in achieving an overview of the spectrum of clinical semiology has been lack of large published series, previous studies having included as few 6 patients 5 and the largest series to date being based on 22 patients. [START_REF] Wang | Electroclinical features of insulo-opercular epilepsy: an SEEG and PET study[END_REF] Thus, although several previous studies involving insulo-opercular seizures exist, these either have fewer cases, [START_REF] Isnard | Clinical Manifestations of Insular Lobe Seizures: A Stereo-electroencephalographic Study[END_REF][START_REF] Proserpio | Insular-opercular seizures manifesting with sleep-related paroxysmal motor behaviors: a stereo-EEG study[END_REF] do not investigate semiologic features, [START_REF] Wang | Electroclinical features of insulo-opercular epilepsy: an SEEG and PET study[END_REF][START_REF] Mullatti | Stereotactic thermocoagulation for insular epilepsy: Lessons from successes and failures[END_REF][START_REF] Freri | Perisylvian, including insular, childhood epilepsy: Presurgical workup and surgical outcome[END_REF] or do not focus on spontaneous seizures. [START_REF] Ostrowsky | Representation of pain and somatic sensation in the human insula: a study of responses to direct electrical cortical stimulation[END_REF][START_REF] Mazzola | Electrical stimulations of the human insula: their contribution to the Ictal Semiology of Insular Seizures[END_REF] In addition, previous studies have not addressed the potential role of specific insulo-opercular functional subregions in different semiologic and propagation patterns. The insula can be divided into seven cytoarchitectonic subdivisions with a posterodorsal to anteroventral gradient of hypergranular to granular, dysgranular, and agranular fields. [START_REF] Morel | The human insula: Architectonic organization and postmortem MRI registration[END_REF] In line with this cytoarchitectonic gradient, a structural anteroposterior connectivity gradient without any distinct borders was shown using diffusion-weighted imaging (DWI). [START_REF] Cerliani | Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex[END_REF][START_REF] Ghaziri | The corticocortical structural connectivity of the human insula[END_REF] Functional connectivity studies also reported two [START_REF] Cauda | Functional connectivity of the insula in the resting brain[END_REF] or four [START_REF] Kurth | A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis[END_REF] distinct regions within the insula along a ventral-anterior to dorsal-posterior gradient. Hence, we postulated that semiologic features of insulo-opercular seizures might correlate with connectional architecture subregions.

The human Brainnetome Atlas (BNA), with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomic and functional connections. [START_REF] Fan | The Human brainnetome atlas: a new brain atlas based on connectional architecture[END_REF] In this atlas, insular cortex is subdivided into six subregions that closely relate to previous functional and histologic maps of the insular cortex. [START_REF] Morel | The human insula: Architectonic organization and postmortem MRI registration[END_REF][START_REF] Kurth | A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis[END_REF][START_REF] Kelly | A convergent functional architecture of the insula emerges across imaging modalities[END_REF] Thus, it provides a very appropriate atlas for insulo-opercular subregion localization based on connectional architecture rather than gyral anatomy.

The present study collected a large series of patients with seizures arising from the insulo-opercular cortex determined by SEEG. Quantified semiologic characteristics and SEEG changes were examined using principal component analysis and cluster analysis to correlate the distinctive semiologies with insulo-opercular subregions.

| PATIENTS AND METHODS

| Patient selection and SEEG recording

We collected patients with drug-resistant focal epilepsy who underwent SEEG in three epilepsy centers (Tsinghua • Four main semiologic subgroups of insulo-opercular seizures were identified by using principal component analysis and cluster analysis. • These groups organized along an anteroventral to posterodorsal axis based on a novel parcellation method, the Brainnetome Atlas. • Semiology, particularly subjective symptoms, significantly correlated with insulo-opercular subregion localization. • Connectional architecture segmentation rather than gyral anatomy may be important in determining neural correlates of complex semiologic patterns.

University Yuquan Hospital, Beijing Tiantan Hospital, and Guangdong Sanjiu Brain Hospital) from July 2014 to June 2018. We included 37 patients with seizures arising from insula ± opercular cortex. The seizure-onset zone was defined from SEEG recordings based on the first clear ictal electrical change, providing: (a) that this change occurred prior to the clinical onset of the seizure, and (b) that it manifested by a fast synchronizing discharge (low-voltage fast activity or recruiting fast discharge of spikes). [START_REF] Bartolomei | Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG[END_REF] We excluded patients whose SEEG recording was inconclusive or in whom seizures arose from adjacent cortex and secondarily spread to insulo-opercular cortex. This study was approved by the Tsinghua University Yuquan Hospital Human Ethics Review Committee (No. 2016005) and informed consent was signed by the patients or their guardians.

All patients underwent comprehensive evaluation, including 3T magnetic resonance imaging (MRI), and long-term scalp video EEG, and SEEG recordings. The SEEG explorations were performed by using intracerebral multiple contact electrodes (HKHS Healthcare Co., Ltd, Beijing, China; 8-16 contacts, length: 2 mm, diameter: 0.8 mm, 1.5 mm apart) that were placed following the guide of Sinovation Robot (Sino Precision Medical Technology Co., Ltd, Beijing, China) or the Leksell Stereotactic System (Elekta, Stockholm, Sweden). For detailed methods, please see Wang et al. [START_REF] Wang | Distinctive epileptogenic networks for parietal operculum seizures[END_REF] Video-SEEG (256 channels Nihon Kohden digital EEG system, Nihon Kohden, China) was recorded for 1-3 weeks in order to record patients' habitual seizures. Signals were recorded with a bandpass filter between 0.08 and 600 Hz and with a sampling rate of 2000 Hz. Interictal and ictal SEEG changes were analyzed using a bipolar montage between adjacent contacts of the electrode, with the reference electrode chosen in the white matter.

| Automatic localization and cortical labeling of depth intracranial electrodes in Brainnetome Atlas

We have developed an integrative toolbox "FIELD" based on the MATLAB software platform to efficiently and accurately reconstructed and labeled the intracranial electrodes implanted in patients with drug-resistant epilepsy. [START_REF] Qin | Automatic and precise localization and cortical labeling of subdural and Depth Intracranial Electrodes[END_REF] In this toolbox, the electrode coordinates were normalized to a standard Montreal Neurological Institute (MNI) space and the probabilistic value of each contact belonging to a specified area or functional network was also provided. The MNI coordinates were projected onto the BNA to obtain accurate localization of all electrode contacts for each patient.

The human Brainnetome Atlas was used for insulo-opercular parcellation. Thus, insular cortex was divided into six functional subregions rather than anatomic gyral parcellation. Subregions of insular cortex according to the BNA included INS1 (G, hypergranular insula), INS2 (vIa, ventral agranular insula), INS3 (dIa, dorsal agranular insula), INS4 (vId/vIg, ventral dysgranular and granular insula), INS5 (dIg, dorsal granular insula), and INS6 (dId, dorsal dysgranular insula). The opercular cortex was defined as consisting of four subregions created from this atlas: OpF (A44v, A44op and A45c); OpC (A4tl); OpP (A1/2/3tonla, A40rv); and OpT (A41/42, TE1.0/TE2.0). In addition, mesial frontal lobe (supplementary motor area, SMA; pre-SMA; anterior/middle cingulate gyrus; and orbitofrontal cortex) and mesial temporal lobe closely connected to insulo-opercular cortex were included.

| Analysis of anatomic- electroclinical features

Ictal clinical and electrical modifications were analyzed in each patient and each seizure following the method developed by a previous study of frontal lobe seizures. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] Video SEEG-recorded clinical semiology and electrical data were independently analyzed by three epileptologists (H. Wang, B. Zhang, and Q. Feng).

All recorded seizures were reviewed, and the presence or absence of 14 ictal signs observed in this series was noted. An overall semiologic score was established for each patient with values ranging from 0 (=absence of that sign), to a maximum of 2 for major features (=constant and early sign present in each seizure), with minor features being scored as 1 (=sign not always present). With regard to semiologic description, the terms that were best able to categorize seizures according to different anatomic involvement were selected, and nondiscriminating signs were removed. Semiologic categories included those motor semiologies described in the previous frontal seizures study. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] In addition, multiple sensory symptoms were listed because of the known repertoire of subjective clinical responses observed during insular and opercular stimulation. [START_REF] Mazzola | Electrical stimulations of the human insula: their contribution to the Ictal Semiology of Insular Seizures[END_REF][START_REF] Maliia | Functional mapping and effective connectivity of the human operculum[END_REF] In contrast, consciousness was preserved during the majority of insulo-opercular seizures, and as such the presence or absence of this sign did not correspond to differences in anatomic localisation. [START_REF] Isnard | Clinical Manifestations of Insular Lobe Seizures: A Stereo-electroencephalographic Study[END_REF] Therefore, the term "impairment of consciousness" was excluded. Orofacial signs included elementary motor and/or sensory signs, such as facial tonic or clonic signs, contraction of mouth corner, and/or tongue or facial anesthesia. We did not count within this group oroalimentary automatisms such as lip smacking or swallowing.

We also estimated the level of participation of each region in initial seizure organization. The regions not involved by SEEG ictal activity were scored as 0, whereas those immediately involved at electrical onset were scored as 2. The intermediate score 1 was used when SEEG ictal activity was seen later. For further detail of this method, the reader is referred to Bonini et al. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] Notably, not only regions involved at seizure onset but also regions involved by early spread during the period of appearance of clinical signs were evaluated and scored.

Two matrices encompassing 14 (clinical signs) and 12 (brain areas) variables, scored 0-1-2 for each of the 37 patients, were thus obtained.

| Statistical analysis

The process of cluster analysis and principal component analysis was similar to the previous study. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] We summarized the process steps as below. Two dissimilarity matrices were computed based on the two matrices (signs × patients and areas × patients), which were entered into hierarchical cluster analysis in MATLAB. Cluster analysis was performed to allow ordering of variables. The proximity of variables within the dendrogram represented their degree of similarity. Ordered sequences of ictal signs and of brain areas were thus obtained, arranged according to the frequency of their co-occurrence. A Kendall correlation matrix was then performed to demonstrate the relationship between ictal signs and involved areas, and to measure the strength of this association. The correlation test was finally used to assess the significance at P < .05 for each correlation.

Then, principal component analysis (PCA) was performed in order to convert all possibly correlated variables (signs) into a smaller number of linearly uncorrelated variables (principal components). Consequently, the size of the transformed data was reduced, and the first three components could explain the variance in the data. Finally, patient position within the new lower-dimensional space supplied by PCA was computed, as defined by ictal signs used as coordinates. Using these new coordinates, hierarchical clustering was performed, aiming to distinguish clinically homogeneous groups of patients.

| RESULTS

| General characteristics

We included 37 patients (17 male, 20 female) with seizures arising from insula ± opercular cortex. Mean age at recording was 18 ± 7.9 years and mean epilepsy duration was 9.7 ± 7 years. Cerebral magnetic resonance imaging (MRI) was negative for 22 patients (59.5%). Subsequent to SEEG exploration, all patients underwent tailored resections that included the insulo-opercular cortex and parts of anterior temporal lobe and mesial temporal structures in four patients. The main postsurgical complication was transient hemiplegia (12/37, 32.4%), especially for middle-posterior insular resection. However, occurrence of persistent hemiplegia was low (2/37, 5.4%). Of patients with at least 1-year follow-up (n = 31), 24 were in International League Against Epilepsy (ILAE) [START_REF] Wieser | Commission Report. Proposal for a new classification of outcome with respect to epileptic seizures following epilepsy surgery[END_REF] class 1(77.4%), 4 patients were in class 2, and 3 patients were in class 3. In terms of histopathology, 27 (73%) had focal cortical dysplasia, 5 (13.5%) had gliosis, and 1 (2.7%) had tuberous sclerosis. The other 4 patients showed nonspecific histopathological change.

A combination of lateral orthogonal SEEG trajectories through the frontoparietal and temporal opercula with an oblique approach through the frontal or parietal cortices was performed to allow larger insular sampling. [START_REF] Afif | Safety and usefulness of insular depth electrodes implanted via an oblique approach in patients with epilepsy[END_REF][START_REF] Isnard | The role of the insular cortex in temporal lobe epilepsy[END_REF] A total of 372 electrodes was implanted, with an average number (mean ±SD) per patient of 10.3 ± 2.6 and an average of 5.2 ± 1.5 electrodes sampling insulo-opercular cortex. Electrode placement symptoms. Only six patients (16.2%) manifested focal to bilateral tonic-clonic seizures. Four patients had reflex seizures, one being eating-evoked and three auditory-evoked. Main semiologic features for each patient are described in Table S1.

F I G U R E 1

| Cluster analysis and anatomic- electroclinical correlations

Ictal signs and brain regions classified with hierarchical cluster analysis are shown in INS3). At the bottom-right corner, somatosensory, non-integrated gestural motor behaviors and asymmetric tonic signs are correlated with the more posterodorsal subregions. Therefore, the matrix forms an anteroventral-posterodorsal gradient according to the occurrence of clinical signs. Such a diagonal structure is conserved for significant correlation (P < .05; Figure 2B, starred squares). Matrix correlation also demonstrates that some clinical signs seem to be more specific to certain subregions: Auditory sensations are correlated only with OpT and somatosensory aura is correlated only with posterodorsal regions. However, some clinical signs such as autonomic signs and focal motor signs are more widely distributed and have weaker localization value.

Hierarchical classification of patients results in clusters of clinically homogeneous patients. A cut was chosen that resulted in four groups (Figures S1 andS2). Clustered groups

F I G U R E 2 Correlation matrix between cortical areas and clinical features, as ordered by hierarchical clustering basing on their reciprocal

distance. A, Clustering of brain regions is shown on the horizontal axis. B, Correlation between areas and signs as a function of the color (orange, positive correlation; blue, negative correlation; starred squares, P < .05) follows a diagonal pattern of association running from anteroventral (top left corner) to more posterodorsal regions (bottom right corner). C, Clustering of clinical signs is shown on the vertical axis. In these two ordered sequences, neighboring regions as well as neighboring signs occur more frequently together than distant ones. The gray star shown in the squares denotes a significant Kendall correlation (P < .05). D, Subdivision of insulo-opercular areas according to the Brainnetome Atlas. [START_REF] Fan | The Human brainnetome atlas: a new brain atlas based on connectional architecture[END_REF] Each area is color-coded for visual illumination. INS1-6, subregion of insular cortex; MTL, mesial temporal lobe;OpT, temporal operculum; OpC, central operculum; OpF, frontal operculum; OpP, parietal operculum; MFL, mesial frontal lobe; Sym-pro-axi-tonic, Symmetric proximal/axial tonic posture; Int-gest-motor, Integrated gestural motor behaviors; Non-int-gest-motor, Non-integrated gestural motor behaviors for clinical signs and regions for the four groups of patients obtained with hierarchical clustering performed on new PCA coordinates were shown (Figure 3). The involved brain areas were analyzed as a function of the resulting homogeneous groups of patients and the characteristics of the early spread networks obtained for each group (Figure 4).

Group 1 (four patients) is characterized by epigastric sensation and integrated gestural motor behaviors and/or feelings of fear/rage. Gustatory sensations could also occur in these patients. Moreover, this group is characterized by the absence of focal motor and orofacial signs (Figure 3, group 1). Significant involvement of INS2, INS3, and INS4, as well as mesial temporal lobes occurred in this group.

Group 2 (nine patients), is characterized by auditory sensations and symmetric proximal/axial tonic seizures. In a similar way to group 1, this group is characterized by the absence of focal motor and orofacial signs (Figure 3, group 2). Significant involvement (value-test >1) of OpT occurred in this group. INS4 could be involved in this group because of adjacent location, but the value-test was <0.5.

Group 3, the largest group, was composed of 17 patients. They manifested elementary orofacial signs and laryngeal signs (Figure 5). Autonomic signs and focal motor signs often appeared in this group compared with the other three groups (Figure 3, group 3). Involved regions included INS5, INS6, OpC, and OpF.

Characteristic semiologic features of Group 4 (7 patients) were somatosensory auras followed by nonintegrated gestural motor behaviors and/or asymmetric tonic signs (Figure 3, group 4). Autonomic signs never occurred and involved regions included INS1 and parietal operculum, with propagation to mesial frontal lobe (SMA, pre-SMA, and anterior/ middle cingulate gyrus).

| DISCUSSION

Here, we report semiologic features of 37 patients with insulo-opercular seizures, correlating electroclinical subgroups with insulo-opercular subregions. To our knowledge, this represents the largest reported series to date. Despite the diverse symptoms and complex anatomy of insulo-opercular cortex, our results allowed identification of electroclinical subgroups according to similarity of semiology by means of PCA and cluster analysis. Moreover, using a novel approach based on the Brainnetome Atlas, which takes into account both structural and functional connectivity, we demonstrate that these subgroups were organized along an anteroventral-posterodorsal axis, rather than according to anatomic gyri of insula cortex as found in previous studies. [START_REF] Proserpio | Insular-opercular seizures manifesting with sleep-related paroxysmal motor behaviors: a stereo-EEG study[END_REF][START_REF] Dylgjeri | Insular and insulo-opercular epilepsy in childhood: An SEEG study[END_REF] F I G U R E 3 Clustered groups for clinical signs (A) and regions (B) for the four groups of patients obtained with hierarchical clustering performed on new PCA coordinates. Full names for clinical signs and regions refer to Figure 2 F I G U R E 4 Cortical regions that characterize the four groups of patients. Brain areas significantly involved in one group than the others are outlined in red. The MTL and MFL that cannot be shown in the brain due to mesial regions are listed beside. The color-coded areas refer to Figure 1D. MTL, mesial temporal lobe; MFL, mesial frontal lobe

| Electroclinical subgroups of insulo- opercular seizures

Cluster analysis was selected as a well-suited method to identify similarities across the heterogeneous subjects. The clustered groups were organized along an anteroventral to posterodorsal axis. Anteroventral seizure organizations predominantly showed limbic system semiology, whereas more posterodorsal regions were associated with semiology mainly involving the sensorimotor system.

In group 1, seizures were organized within anteroventral insular regions including INS2, INS3, and INS4, characterized by epigastric sensations, integrated gestural motor behaviors, as well as feelings of fear and rage. These seizures shared features also seen in mesial temporal lobe seizures (MTLS) and prefrontal seizures. The mesial temporal lobes showed significant involvement in the present study, but on the other hand, orbitofrontal and anterior cingulate regions were not necessarily involved, even in fear-related behaviors. The next more posterior ventral group, which significantly involved temporal operculum, was characterized by auditory sensations and symmetric proximal/axial tonic signs. The auditory features can be readily understood by the fact that Heschl gyrus is buried within the lateral sulcus. Auditory sensations are also obtained in response to stimulation of the posterior insula, and especially in the midposterior long gyrus of the insula. [START_REF] Mazzola | Electrical stimulations of the human insula: their contribution to the Ictal Semiology of Insular Seizures[END_REF] INS4 corresponding to ventral posterior insula could be involved in this group. However, according to the lower value-test statistic, INS4 was not classified within group 2. Group 3 was a large and intermediate one in the vicinity of insular central sulcus characterized by elementary orofacial signs and laryngeal signs. Orofacial signs included asymmetric facial tonic or tonic-clonic signs and unilateral eyelid blinking. These signs seem to reflect frontocentral operculum involvement. Laryngeal signs included uncomfortable subjective sensations ranging from constriction to strangulation, often associated with vocalization. In accordance with results from electrical stimulations of the human insula, throat constrictive sensations were obtained from contacts located around, or rostral to, the central insular sulcus. [START_REF] Mazzola | Electrical stimulations of the human insula: their contribution to the Ictal Semiology of Insular Seizures[END_REF] In previous reports of visceral sensations elicited by cortical electrical stimulations, oropharyngeal sensations were most related to the opercular part of the precentral and postcentral gyri and the insula, whereas other digestive sensations including epigastric sensations and retrosternal sensations, were preferentially related to the temporal lobe and the anterior cingulate cortex, respectively. [START_REF] Mulak | Brain mapping of digestive sensations elicited by cortical electrical stimulations[END_REF] Therefore, the laryngeal symptoms strongly pointed to the involvement of dorsal central insula and frontocentral operculum categorized here as group 3. In group 4, seizures arising from posterodorsal insulo-opercular regions were typified by somatosensory auras followed by nonintegrated gestural motor behaviors and/or asymmetric tonic signs. Somatosensory auras were described as electrical paresthesia, anesthesia, pain, or burning sensations, which reflects the role of the parietal operculum in networks subserving basic and higher-order somatosensory processing. [START_REF] Eickhoff | Anatomical and functional connectivity of cytoarchitectonic areas within the human parietal operculum[END_REF] Nonintegrated gestural motor behaviors including hyperkinetic movements without emotional content have been reported in seizures arising from premotor and lateral prefrontal regions [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] and the temporal lobe, [START_REF] Vaugier | Neural networks underlying hyperkinetic seizures of "temporal lobe" origin[END_REF] and also from operculo-insular and parietal lobes. [START_REF] Gibbs | Clinical features of sleep-related hypermotor epilepsy in relation to the seizure-onset zone: A review of 135 surgically treated cases[END_REF] This is proposed to reflect seizure spread to mesial frontal lobes including SMAs and cingulate gyrus. [START_REF] Wang | Distinctive epileptogenic networks for parietal operculum seizures[END_REF][START_REF] Hagiwara | How can we explain the frontal presentation of insular lobe epilepsy? The impact of non-linear analysis of insular seizures[END_REF] The pattern of hyperkinetic movements seen here in posterodorsal regions was in contrast to the anteroventral group 1 in which gestural motor behaviors like grasping and groping were integrated and naturalistic, in keeping with the anteroposterior gradient seen in frontal lobe seizures. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] Nonintegrated behaviors in group 4 cases were frequently associated with tonic posture, as also reported in frontal seizures. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] In the present series, these were asymmetric tonic signs, as has been described previously in insulo-opercular cases, [START_REF] Proserpio | Insular-opercular seizures manifesting with sleep-related paroxysmal motor behaviors: a stereo-EEG study[END_REF] particularly those arising from posterior insula. Mesulam and Mufson proposed a cytoarchitectonic organization in the insula with three major domains: agranular, dysgranular, and granular, concentrically arranged around the piriform olfactory cortex at the limen insula. [START_REF] Mesulam | Insula of the old world monkey. I. Architectonics in the insulo-orbito-temporal component of the paralimbic brain[END_REF] Morel et al divided insular cortex into seven architectonic subdivisions (G, Ig, Id1-3, Ia1-2) with a posterodorsal to anteroventral gradient of hypergranular to granular. All the cytoarchitectonic and connectivity data reflect a functional and structural gradient within the insula from the anteroventral to the posterodorsal parts.

In the present study, we introduce the human Brainnetome Atlas, which contains information on both anatomic and functional connections. [START_REF] Fan | The Human brainnetome atlas: a new brain atlas based on connectional architecture[END_REF] This atlas compares favorably to other parcellation methods. [START_REF] Arslan | Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex[END_REF] The insular cortex has been subdivided into six subregions in this atlas, which relate closely to previous functional and histological maps of the insular cortex. In line with the cytoarchitectonic and functional gradient, the axis from group 1 to group 4 corresponded to the gradient from agranular cortex in the anteroventral social-emotional region to dysgranular and granular cortex in the intermediate orofacial sensorimotor region to hypergranular cortex in the posterodorsal somatosensory and pain region.

Mazzola et al proposed a bipolar organization of major insular functions through direct cortical stimulations: a posterior part assigned to somatosensory functions, and notably to pain perception; and an anterior part assigned to visceral sensitivity and visceromotor functions. [START_REF] Mazzola | Functional mapping of the human insula: data from electrical stimulations[END_REF] This hypothesis is congruent with semiologic features seen in the present study. Furthermore, they proposed that the mid-dorsal insula (an intermediate region in the present study) may be an integrated oral sensory region evoking gustatory, olfactory, and somatosensory sensation.

| Subjective symptoms of insulo- opercular seizures

The insula is considered as a highly eloquent cortical structure, since 80% of electrical stimulations elicited a clinical response in previous studies. [START_REF] Mazzola | Functional mapping of the human insula: data from electrical stimulations[END_REF] Moreover, the insula is the only cortical region where stimulations can evoke such a multi-modal representation, perhaps supporting its integrative functions of polymodal inputs. [START_REF] Mazzola | Functional mapping of the human insula: data from electrical stimulations[END_REF] Insular cortex stimulations through stereotactic electrodes can often reproduce ictal semiology, especially subjective symptoms. In the present study, several subjective symptoms such as epigastric, auditory and somatosensory sensations were indeed strongly discriminating clinical indicators for the identified groups. This localizing importance of subjective semiology in insuloopercular seizures is in contrast to a previous study of frontal lobe seizures in which motor semiology was the key factor allowing clinical categorization. [START_REF] Bonini | Frontal lobe seizures: from clinical semiology to localization[END_REF] On the other hand, specific patterns of motor signs were also associated with certain localizations in the present series.

| Limitations

Our study has some limitations, the main one being that, as for any intracranial EEG method, SEEG sampling was necessarily limited to those sites deemed necessary for surgical pre-evaluation. As such, we cannot exclude the possibility that some nonexplored regions, especially those connected to insulo-opercular regions, had some involvement in initial seizure organization. On the other hand, the clear anatomic electroclinical correlations shown here and the favorable surgical results obtained in the vast majority of cases tend to reinforce the interest of these data. Although the current series represents the largest to date, as with any group-level analysis a larger data set would allow even more fine-grained anatomic electroclinical correlations.

| CONCLUSIONS

Insulo-opercular seizures can be categorized in terms of clinical semiology and correlated with connectional architecture subregions along an anteroventral -posterodorsal axis, in line with the cytoarchitectonic gradient rather than gyral anatomy of insula cortex. Review of a large series allows the full spectrum of semiologic features to be assessed. Subjective symptoms were the key indicators for clinical categorization, although certain motor patterns were also discriminating. While epilepsy is a network disorder [START_REF] Bartolomei | Defining epileptogenic networks: contribution of SEEG and signal analysis[END_REF][START_REF] Kramer | Epilepsy as a disorder of cortical network organization[END_REF][START_REF] Richardson | Large scale brain models of epilepsy: dynamics meets connectomics[END_REF] and frontal, prefrontal, temporal, or parietal seizures can share common semiologic features with insulo-opercular seizures, such semiologic categorization of insulo-opercular seizures provides new insights in facilitating differential diagnosis and presurgical localization. Through use of a novel parcellation method, the Brainnetome Atlas, the present results also highlight the importance of considering connectional architecture when investigating neural correlates of complex semiologic patterns. Future work should take into account not only anatomic relations of structures involved in epileptic networks, but also dynamic aspects such as discharge frequency and coherence that contribute to semiologic expression. [START_REF] Chauvel | Emergence of semiology in epileptic seizures[END_REF][START_REF] Aupy | Insulo-opercular cortex generates oroalimentary automatisms in temporal seizures[END_REF][START_REF] Mcgonigal | Semiology and epilepsy networks[END_REF]

Figure 2 .

 2 These clusters indicate that ictal signs in one group are most frequently present together during seizures. Cluster analysis of the brain regions allows grouping together of anterior ventral regions (INS2, INS3, INS4), posterior ventral regions(OpT), intermediate regions (INS5, INS6, OpC, OpF) and posterior dorsal regions (INS1, OpP). Smaller clusters of insulo-opercular subregions grouped together at lower cluster levels also show strong associations: INS2 and INS3; INS5 and OpC; and INS1 and OpP (Figure 2A). Matrix correlation between clinical signs and involved regions shows a diagonal pattern of correlation (Figure 2B). Yellow-orange squares indicate positive correlation and blue squares indicate negative correlation. At the top-left corner, epigastric sensations and integrated motor behaviors are correlated with subregions located in the more anteroventral regions (INS2 and

F

  I G U R E 5 Anatomic-electroclinical features of a patient from group 3. A, Semiology is characterized by laryngeal discomfort followed by laryngeal constriction with open mouth, and right facial and arm tonic signs. B, Ictal stereoelectroencephalography (SEEG) shows rhythmic spikes evolving to a low-voltage rapid discharge at electrical onset in the intermediate insular region and central operculum. C, SEEG implantation scheme with electrode labels on a three-dimensional (3D) reconstruction of the brain; electrodes A, B, C are oblique, and the others are orthogonal. D, Sagittal view of patient's T1 MRI with implanted electrodes. Regions showing major involvement in the generation of the ictal discharge are colored in orange

6

 6

4. 2 |

 2 Anteroventral-posterodorsal axis based on connectional architecture

  Electrode position in standard MNI space. Entry (red dots) and target (blue dots) points obtained from each electrode for each patient (n = 37) are displayed in the left (A) and right (B) hemisphere. Top and bottom rows show lateral and medial views with different transparencies for visual illustration, respectively including numbers of electrodes sampling insula and operculum subregions for each patient is listed in Table1. Electrode position in standard MNI space is shown in Figure1.Seizures were frequently nocturnal (23/37, 62.2%) and often occurred daily (29/37, 78.4%). Almost all patients (34/37, 91.9%) had seizures with preserved awareness at the beginning. Hence, 32 patients (86.5%) reported clear subjective signs including somatosensory (10/32), auditory sensation (10/32), laryngeal sensation (8/32), and autonomic sensation (6/32). However, no patient had vestibular or olfactory Regions sampled by SEEG implantation for each patient

	T A B L E 1 Pts	SEEG explored	Opercular electrodes/ number	Insular electrodes/number	SOZ
	1	L:INS, Op, T	OpF/2; OpC/1; OpP/2; OpT/1 INS1/2; INS5/1; INS6/1	INS5
	2	R:INS, Op, P, C, CG	OpP/2	INS1/1	OpP
	3	R:INS, Op, C, T	OpF/2; OpC/1; OpP/1; OpT/2 INS1/1; INS5/1; INS4/1; INS2/1	INS1 + OpP
	4	R:INS, Op, F, CG	OpF/1; OpC/1; OpP/2; OpT/1 INS1/2; INS5/1; INS6/1; INS4/2; INS3/1 INS1 + OpP
	5	R:INS, Op, F, CG, C, T	OpF/1; OpC/1; OpP/3; OpT/1 INS1/1; INS5/1; INS6/1; INS4/1; INS3/1 INS5
	6	R:INS, Op, F, CG	OpF/2; OpC/1; OpP/2	INS1/1; INS5/1; INS3/1	INS1 + OpP
	7	L:INS, Op, F, C, T	OpF/1; OpC/1; OpP/2	INS1/2; INS5/2; INS6/3; INS2/1; INS3/1 INS5 + OpC
	8	L:INS, Op, F, C, CG	OpF/1; OpC/1; OpP/2	INS1/2; INS5/1; INS6/2; INS3/1; INS4/2 INS1 + OpP
	9	Bilateral: INS, Op; L: T	OpC/1; OpP/3; OpT/1	INS1/3; INS5/3; INS6/1; INS4/1	INS5
	10	L:INS, Op, F, CG; R: F	OpF/1; OpC/1; OpP/1; OpT/1 INS1/1; INS5/1; INS6/2	OpT
	11	R:INS, Op, T, CG, P	OpF/1; OpC/1; OpP/1	INS1/1; INS5/2; INS4/1	INS5 + OpC
	12	Bilateral: INS, Op; L: F, CG	OpF/2; OpC/2; OpP/3	INS5/3; INS6/2; INS2/1; INS3/1	OpC
	13	Bilateral: INS, Op; L: T	OpC/1; OpP/3; OpT/3	INS1/4; INS5/1; INS4/3; INS2/1; INS6/3 OpT
	14	R:INS, Op, T, P	OpF/2; OpT/2; OpP/2	INS1/2; INS6/1; INS4/1; INS3/1	OpT + INS1
	15	R:INS, Op, T, F	OpC/1; OpP/2; OpT/3	INS4/1; INS5/1	OpT
	16	L:INS, Op, T	OpF/1; OpC/1; OpT/2; OpP/2 INS1/2; INS5/1; INS6/1; INS2/1; INS3/1 INS5 + INS6
	17	R:INS, Op, F, C, CG	OpF/1; OpC/2; OpP/2	INS5/1; INS6/1	OpC
	18	R:INS, Op, F	OpF/2; OpC/2;	INS5/1; INS6/2; INS3/1	INS5 + INS6
	19	R:INS, Op, C, F	OpF/1; OpC/2; OpP/1	INS5/2; INS6/1; INS3/1	INS5 + INS6
	20	L:INS, Op, T	OpF/1; OpC/1; OpP/2; OpT/2 INS1/1; INS5/2; INS4/1; INS2/1; INS3/1 INS2
	21	R:INS, Op, T, F, CG	OpF/2; OpT/1	INS2/1; INS3/1; INS4/1	INS2 + INS3
	22	R:INS, Op, F, T	OpF/1; OpC/1; OpP/1	INS5/2; INS6/1; INS3/1; INS4/1; INS2/1 INS5 + OpC
	23	R:INS, Op, F, T	OpF/1; OpC/1	INS2/1; INS3/1; INS4/1; INS6/1	INS2 + INS3
	24	L:INS, Op, C, P	OpF/1; OpC/1; OpP/1; OpT/2 INS1/2; INS5/1; INS6/1; INS4/1	INS1
	25	R:INS, Op, T, C	OpC/1; OpT/1	INS1/1; INS5/1; INS2/1; INS4/1	OpT
	26	L:INS, Op, T	OpT/2	INS1/1; INS2/1; INS4/2	OpT + INS1
	27	Bilateral: INS, Op, T; R:CG	OpF/1; OpC/1; OpP/1; OpT/1 INS5/2; INS6/1; INS4/2; INS3/2	INS5 + INS4
	28	L:INS, Op, F, CG	OpF/1; OpC/1	INS2/1; INS3/1; INS6/1	INS2 + INS3
	29	L:INS, Op, C, F, P, CG	OpF/1; OpC/1; OpP/1; OpT/1 INS1/1; INS5/1; INS3/1; INS6/1; INS4/1 INS5
	30	R:INS, Op, F	OpF/2; OpC/1; OpT/1	INS1/1; INS5/1; INS3/1; INS6/1; INS4/1 INS5 + OpC
	31	Bilateral: INS, Op, F, T	OpF/2; OpC/1; OpT/1	INS2/1; INS3/2; INS6/2; INS4/1	INS2 + INS4
	32	L:INS, Op, F, T	OpF/1; OpC/1	INS5/1; INS6/1	INS5 + INS6
	33	L:INS, Op, F, T	OpF/2; OpC/1; OpT/1	INS2/1; INS3/1; INS6/1; INS5/1	OpF
	34	R:INS, Op, F, C	OpF/1; OpC/1	INS5/1; INS6/1	INS5
	35	R:INS, Op, T	OpF/1; OpC/1; OpP/1; OpT/1 INS1/1; INS5/1; INS6/1; INS4/1	INS5 + OpC
	36	R:INS, Op, C, CG, F, P	OpC/1; OpP/1; OpT/1	INS1/2; INS5/1; INS6/1	OpP
	37	R:INS, Op, T, P, CG	OpC/1; OpT/1	INS1/1; INS5/2; INS6/1	INS5

Abbreviations: C, central lobe; CG, cingulate gyrus; F, frontal lobe; INS, insula; INS1-6, subregion of insular cortex; L, left; Op, operculum; OpF/C/P/T, frontal/ central/parietal temporal operculum; P, parietal lobe; R, right; SOZ, seizure-onset zone; T, temporal lobe.

ACKNOWLEDGMENTS

The authors would like to appreciate Professor Lingzhong Fan(Institute of Automation, Chinese Academy of Sciences)for his helpful advice on use of the Brainnetome Atlas. Liang Wang was supported by the Natural Science Foundation of China (31771255), the Beijing Municipal Science and Technology Commission (Z171100000117014), and the Strategic Priority Research Program of Chinese Academy of Science (XDB32010300).

Natural Science Foundation of China, Grant/Award Number: 31771255, Z171100000117014 and XDB32010300; Beijing Municipal Science and Technology Commission; Strategic Priority Research Program of Chinese Academy of Science

CONFLICTS OF INTEREST

None of the author has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section.


Robin Tournemenne
email: [robin.tournemenne@irccyn.ec-nantes.fr

Jean-François Petiot
email: jean-francois.petiot]@irccyn.ec-nantes.fr

Joël Gilbert
email: joel.gilbert@univ-lemans.fr

The Capacity forSimulation by Physical Modeling to Elicit Perceptual Differences Between Trumpet Sounds

This paper investigates the subjects' ability to perceive trumpet sound changes, the sounds being either simulated by physical modeling or played by areal musician. The objective of the paper is to study to what extent simula-tions are able to create perceptually distinct sounds and to compare the discrimination on simulated sounds to the discrimination obtained when the instrument is played. Twoexperimental factors are considered to study percep-tual discriminations by apanel of subjects: changes in the resonator geometry (instrument discrimination) and changes in the sound dynamics of the same instrument (dynamics discrimination). The changes in the resonator geometry are made with tiny geometrical variations of aparameterized leadpipe, whereas changes in dynamics are generated modifying control parameters for simulated sounds and playing dynamic instructions for real mu-sicians. The same note (Bb4) issimulated using the harmonic balance technique and played by areal musician. To assess the discrimination, a"same-different" test has been carried out involving apanel of 24 listeners and pairs of sounds coming from the simulations or the recordings (for the sake ofthe experiment, the recorded sounds are resynthesized). Forthe different experimental conditions (differences between trumpets or between dynamics), the abilities of the listener to discriminate different sounds and to identify identical sounds are ana-lyzed. Using the ReceiverOperating Characteristics plots, the results show that even if the differences are more noticeable with sounds played by the musician, simulations are able to produce noticeable differences between the instruments, mainly due to playing frequency differences. They confirm the capacity of the physical model to represent realistic playing conditions for the trumpet.

Introduction

Investigating physical models of musical instruments is an interesting wayofraising knowledge about their functioning, and may provide better assistance in their design. Ye t, assistance in instrument design using physical modeling simulations is still at an early stage [START_REF] Campbell | Brass instruments as we knowt hem today[END_REF] and further work is still necessary to enhance its reliability.A ni nteresting comparison of the sounds of reed instruments of different types (single or double reed)i sm ade using time domain simulations in [START_REF] Carral | Single vs double reed conical woodwind sounds: Where does the difference lie? Proceedings Forum Acusticum[END_REF]. With huge variations in the bore of the instrument (two instruments of different families), the results showthat simulated sounds and real sounds exhibit the same differences. Time domain simulations are also described in [START_REF] Woodhouse | Perceptual thresholds for acoustical guitar models[END_REF] to investigate perceptual differences between guitar sounds with psychoacoustical testing. These experiments investigate just noticeable differences between simulated sounds from different guitars, even if the "virtual" modifications of the instrument are not necessarily realistic and cannot be manufactured. The ability for sound simulations to predict the sound characteristics of ar eal instrument through physical modeling is an interesting challenge for the computer-aided design of instruments. It needs to integrate different phases to confirm that simulations can represent the sounds of real instruments. The first phase consists in choosing arelevant physical model to represent the functioning of the instrument [START_REF] Adachi | Trumpet sound simulation using atwodimensional lip vibration model[END_REF][START_REF] Välimäki | Discrete-time modelling of musical instruments[END_REF]. Then, once ap hysical model simulating sounds is chosen, asecond phase consists in defining to what extent the simulated sounds are in agreement with their equivalent real sounds played by "real" musicians on real instruments [START_REF] Carral | Single vs double reed conical woodwind sounds: Where does the difference lie? Proceedings Forum Acusticum[END_REF]. In [START_REF] Petiot | Comparison of trumpets' sounds played by am usician or simulated by physical modelling[END_REF], the authors focused their work on hows imulations by physical modeling could be used to predict certain characteristics of trumpet sounds liket he playing frequency, the spectral centroid and the spectral irregularity.These objective measurements are interesting for instrument comparisons, butperceptual aspects of the sounds must be addressed to consider simulations as an aided design tool. Athird phase then concerns the study of the agreement between simulations and real sounds from ap erceptual point of view. [START_REF] Tournemenne | Study of perceived differences between simulated and real trumpets sounds[END_REF] presents afi rst study on the noticeable differences between sounds, whether they be simulated or real. The present paper is in the continuum of this work and focuses on perceptual aspects. While based on the same experimental data as [START_REF] Tournemenne | Study of perceived differences between simulated and real trumpets sounds[END_REF], the present paper undertakes amore complete analysis of the perceptual differences between sounds. Likeinprevious papers, the harmonic balance technique [START_REF] Gilbert | Calculation of the steady-state oscillations of ac larinet using the harmonic balance technique[END_REF] is used to simulate the sounds, with different control parameters for the simulation. Likewise, small variations in the internal geometry of the trumpet are made by controlling the geometry of the leadpipe, an important part of the bore connecting the mouthpiece to the tuning slide. The general aim of this paper is to study whether simulations through physical modeling are able to produce perceptible differences between instruments. An additional objective is to compare the perceptive differences obtained with the simulations to those obtained with ar eal trumpeter playing the instruments. The present study does not focus on the nature of the perceptive differences, butfocuses only on afirst step in their magnitude. Forthis reason, the perceptual test considered in this study is ad iscrimination test that aims to study whether differences between sounds are detected. Different protocols can be proposed for ad iscrimination test, liket he ABX test [START_REF] Boley | Statistical analysis of abx results using signal detection theory[END_REF] or the 2A FC (2 alternative forced choice)protocol [START_REF] Bi | Discrimination tests and measurements[END_REF]. The latter is generally used when the underlying dimension can be specified. Giventhat the differences between the sounds proposed in the experiments are extremely subtle, according to timbre and pitch, the present study considers asimple protocol called the samedifferent test, or AX test [START_REF] Lawless | Quantitative sensory analysis. Psychophysics, models and intelligent design[END_REF]: apair of sounds is presented, the participant is simply asked to indicate if the twosounds are different or the same. This protocol, simpler than the ABX test because only twos ounds are played before the decision of the participant, is retained for the simplicity of its implementation. The paper focuses on twoa spects of discrimination: [START_REF] Campbell | Brass instruments as we knowt hem today[END_REF] discrimination between different instruments, obtained by small variations of the geometry of the resonator.Inthis case, pairs of sounds include two sounds generated by different instruments; (2) discrimination between different dynamics of the same instrument, obtained by varying the control parameters of the simulations, or the instructions givent ot he trumpeter.I nthis case, pairs of sounds include twosounds generated by the same instrument. Twoc ategories of sounds are proposed for the discrimination tests: simulated sounds, obtained by simulations by physical modeling, and real sound, obtained by the recording of areal trumpeter.Itisimportant to note that the protocol for the test will nevermix asimulated sound and areal sound to constitute apair (these two categories of sounds being very different): the pairs are either simulated or real. The results expected from this study concern the abilities of simulations to introduce changes in the timbre or pitch of an instrument, and an assessment of howt hese changes are perceptible. An additional result concerns the indirect comparison of the results of the discrimination tests for the simulated sounds, and the real sounds. Section 2p rovides details on the physical model of trumpets, the simulation method and its parameters and the description of the instruments used in the experiments. Section 3describes the material and methods of the experiments, through the presentation of the same-different test, the design of the stimuli used for instrument and dynamics discrimination and the data analysis method. Section 4 describes the results and adiscussion about the abilities of simulation to elicit perceptual differences between trumpet sounds. The main conclusions of this study are drawn in Section 5.

Background on the physical modeling of at rumpet

Physical model and harmonic balance technique

The trumpet is awind instrument that produces sounds by ac oupling of an excitator,t he lips of the musician, to a resonator,t he body of the instrument. The characteristics of the played note (frequency, timbre)depend on the inner shape of the resonator (called the "bore")a nd of course on the musician'sability.From an overpressure P m in the mouth of the musician (from 1kPa to about 10 kPa),t he lips act as av ibrating valvet hat modulates the air flow into the instrument [START_REF] Campbell | Brass instruments as we knowt hem today[END_REF]. The column of air in the instrument vibrates, according to the resonance frequencies of the resonator.Ar egime of oscillations is created, result of acoupling between the resonator and the lips. It is important to mention that this coupling is the result of the reaction of the resonator on the lips: vibrations of the lips are facilitated at frequencies which correspond to the resonance frequencies of the bore. With the same bore, several notes can be obtained, corresponding to the different resonance frequencies.

To obtain ac hromatic scale, the musician uses valves changing the bore length, thus the resonance frequencies. The main measurement characterizing the resonance frequencies of the resonator alone is the input impedance Z (Figure 3).Itrepresents the response of the instrument in forced oscillations for agiven frequencyrange and shows several peaks (corresponding to resonances)that are used to play the different notes. The basic physical model of the trumpet is described by three equations (Equation 1, 2, 3) that represent the behavior of the different parts of the system. See for example, [START_REF] Campbell | Brass instruments as we knowt hem today[END_REF].

p(jω) = Z(jω)v(jω), (1)
d 2 H(t) dt 2 + 2πf l Q l dH(t) dt + (2πf l ) 2 (H(t) -H 0 ) = P m -p(t) µ l , ( 2
)
v(t) = bH (t) 2 P m -p(t) ρ . ( 3
p(t) = N n=1 A n cos 2πnF 0 t + ϕ n . ( 4
)
Assuming that the solution p(t)ofthe system of equations is harmonic (Equation ( 4)), the unknowns of the simulations are the amplitudes of the harmonics A n ,t he phases ϕ n and the playing frequency F 0 .Anumerical solution p(t) satisfying equations ( 1),( 2),(3) can be defined if the system converges towards astable solution (more details can be found in [START_REF] Gilbert | Calculation of the steady-state oscillations of ac larinet using the harmonic balance technique[END_REF]). From the values of the amplitudes A n , the phases ϕ n and the playing frequency F 0 ,asteady state sound can then be synthesized by additive synthesis.

Let us not forget that the approached solution p(t)computed from the elementary model corresponds to an acoustic pressure inside the instrument mouthpiece. In order to obtain the acoustic pressure outside the resonator,a tt he levelo ft he bell, as pectrum transformation function is used, according to the works of Benade [START_REF] Benade | Relation of air-column resonances to sound spectra produced by wind instruments[END_REF]. The resonator is considered in this case as ah igh pass filter,t he filter'se nvelope corresponding to the transfer function of the instrument. In practice, giventhat the instruments considered in the study are very similar,the same filter (cutoff frequency = 2000 Hz)i sused for every instrument (computations showthat differences between the transfer function of the different instruments are negligible). The amplitude of the harmonics of the external sound are computed by multiplying directly the magnitude of each harmonic A n by the filter'sm agnitude response. This generates the amplitudes a n of the harmonics (n = 1toN)c haracterizing the external sound of the trumpet.

Control parameters of the simulations

To simulate sounds, it is necessary to define relevant values (values that lead to ac onvergence towards as teady state sound)f or the parameters of the virtual musician. These values are obtained from [START_REF] Cullen | Brass instruments: Linear stability analysis and experiments with an artificial mouth[END_REF] and by as ystematic exploration of the parameters' space [START_REF] Petiot | Comparison of trumpets' sounds played by am usician or simulated by physical modelling[END_REF] that ensures convergence of the numerical model. One particular note, Bb4 (concert-pitch, F 0 = 466.16 Hz), located in the middle of the range of the trumpet, waschosen as atarget sound for the experiments. This note corresponds to the fourth oscillation regime of the trumpet. Twov irtual musicians vm1 and vm2are considered in this study to simulate this note.

The values of the control parameters of the simulations, that lead to aconvergence toward aperiodic solution, are giveninT able I. We are aware that the values of the pressure in the mouth P m are not realistic for ar eal musician (theyare too high). But these values are necessary to create the oscillations with the chosen (minimal)physical model. Giventhat the impedance measurement is limited in frequency( Figure 3),i ti su nnecessary to takei nto account manyh armonics for the computation of the simulated sounds. Above 2500 Hz, the magnitude of the impedance is flat and no difference between trumpets is noticeable. Forthis reason, the number N of harmonics considered for the simulations waslimited to not exceed 2500 Hz. Forthe note Bb4 (466.16 Hz), this corresponds roughly to N = 6 harmonics. All the sounds were then simulated with N=6 harmonics (examples of spectra of raws imulated sounds are giveninFigure 5).Inconclusion, for agiven trumpet, characterized by its input impedance Z,t he simulations give the playing frequencyo ft he sound, the amplitudes and phases of its first six harmonics, according to the set of parameters describing the virtual musician (P m , µ l , f l , b, H 0 , Q l ). Raw simulated sounds,corresponding to the Table II. Description of the dimensions (inmm) of the three leadpipes A, B, Co ft he study.N .B .: The radius of the last part r 5 is not ap arameter,i ti sc onstant for all the leadpipes and corresponds to the cylindrical bore radius of the trumpet (Bach trumpet Medium Large (ML) bore, .459"). pressure at the bell of the instrument, can then be synthesized by additive synthesis for agiven duration.

Parameterized leadpipe

The different instruments considered in this study are obtained by small variations in the inner shape of the trumpet, called the bore. These variations concern atube called the leadpipe, an important part of the bore that connects the mouthpiece to the tuning slide. This part, roughly conical, has agreat influence on the intonation and the timbre of the instrument (Figure 1) [START_REF] Petiot | Comparison of trumpets' sounds played by am usician or simulated by physical modelling[END_REF]. Ap arameterized leadpipe was designed, divided into 4parts of equal length (l=55mm), corresponding to aquarter of the length of astandard one (Figure 2).The parts were manufactured with various internal input and output radii. Giventhat we only consider continuous leadpipe profiles (the output radius of the n -1 part is equal to the input radius of the n part), the leadpipe is parameterized by 4radii r 1 , r 2 , r 3 , r 4 . Three leadpipes, named A, B, C, were considered for the study.T able II shows the radii values of these leadpipes.

Leadpipes Ba nd Ca re very similar,w ith variations in the order of some tenths of mm. Leadpipe Ccorresponds roughly to the shape of the standard one provided with the trumpet. Leadpipe Apresents significant variations in the inner shape (inthe order of amillimeter compared with the twoothers). Next, these leadpipes are assembled with the rest of the trumpet (a Bb trumpet, Bach model Ve rnon, bell 43). The trumpets are named A, B, Ci nt he rest of the paper for simplicity.I ti sinteresting to mention that trumpets Ba nd Ca re very similar when played (trumpeters are generally unable to distinguish them in ab lind test), while trumpet Ai sv ery different to the twoo thers (players always recognize this leadpipe), due to intonation and timbral characteristics. Using the same mouthpiece (Yamaha 15B4), the input impedances Z of these three trumpets were measured with an impedance sensor developed by Dalmont and Le Roux [START_REF] Dalmont | An ew impedance sensor for wind instruments[END_REF] in an anechoic chamber.This measurement is the input of the sound simulations described in the previous section. To visualize the importance of the differences between the three trumpets, the magnitude of Z is presented in Figure 3. One notices that the differences between the trumpets concerning impedance are in agreement with the difference concerning the geometry: Band Care very close, while Aisdistinct from Band C.

Material and methods

Description of the perceptual tests

As ame-different test [START_REF] Lawless | Quantitative sensory analysis. Psychophysics, models and intelligent design[END_REF] wasi mplemented to study instrument discrimination and dynamics discrimination. The principle of this paired test is straightforward: in as ingle trial, as ingle pair of same or different sounds is presented; the participant is invited to indicate whether the twosounds are the same or different. The participant could play ap air several times and had to listen to it at least once. Headphones (identical for every participant, Beyerdynamic DT 990)a nd ac omputer interface were used to play the pairs. The participant wasfree to adjust the audio volume of the stimuli. The graphical interface for the test is giveninFigure 4for information. The pairs of sounds were constituted of twoeither simulated or real sounds. Simulated and real sounds are never associated in the same pair,g iven that theya re very different in timbre (simulated sounds have only 6h armonics -p layed sounds have 15 harmonics, see Figure 5).A detailed description of the pairs presented to the participants is giveninthe next section. 24 participants (9 women and 15 men -ages spanned from 20 to 23 years old)took part in the hearing experiment. All participants, engineering students, had normal hearing and aregular practice of am usical instrument. Fori nstrument discrimination, two playing instances of the same instrument were included in the test in order to increase the diversity of sounds. Concerning simulated sounds, these twoinstances correspond to the twovirtual musicians vm1and vm2. Regarding real sounds, theyc orrespond to twod istinct recordings of the instrument. The panel of subjects were divided in 2groups of 12 participants. Fors imulated sounds, the first group (respectively the second group)l istened to sounds generated by the virtual musician vm1( respectively vm2) and for processed real sounds, the first group (resp. the sec-ond group)listened to real sounds played by amusician in afi rst recording (resp. in as econd recording). Givent hat the results of the tests for the twogroups were similar,in the end, all the data for the twogroups were gathered.

Definition of the sound stimuli

Forevery experiment, the note generated to constitute the stimuli wasaB b4 (concert pitch)c orresponding to the fourth regime of the trumpet, with no valvep ressed. The tuning slide of the trumpet wasa djusted to the same position (pulled 1cm)f or all the measurements (impedance measurements -r ecording of the notes). The three trumpets A, B, Cw ere considered for instrument discrimination, and only trumpet Cwas considered for dynamics discrimination. Every sound of the study were generated by additive synthesis (described thoroughly in this section) and their duration wast he same (0.75 s).A50ms linear envelope slope wasadded at the beginning and end of each sound. Every sound wasn ormalized in amplitude so that the peak amplitude of the pressure remains in the interval (-1, +1).E ventually,p airs of sounds were constituted of twosounds, separated by a250 ms space.

Instrument discrimination

The objective of this first experiment is to study whether the differences between the sounds of the three trumpets A, B, Care perceptible, the sounds being either played by am usician or simulated with the harmonic balance technique. Since there are 3i nstruments, 3c omparisons are considered for the same-different test (A versus B, Bversus Ca nd Av ersus C).F or each comparison, pairs of sounds coming from different categories are considered in the test. These categories are described thereafter.

Simulated sounds: This category corresponds to sounds

obtained by simulations of the physical model. Foreach trumpet, and each virtual musician vm1a nd vm2( see Table I),t he playing frequency F 0 ,t he amplituden a n , and phases ϕ n of the six first harmonics representing the pressure at the bell of the instrument were computed. The sounds were next generated by additive synthesis (duration = 0.75s). This set is called raw simulated sounds.The spectra of the trumpets for the virtual musician vm1are plotted in Figure 5(left column)f or information.

Resynthesized Real sounds: This category corresponds

to sounds played by amusician. Forthe 3trumpets, the same note Bb4 wasr ecorded with the same musician. All the recordings (sampling frequency4 4100 Hz, 16 bits)w ere made in the same room with aS hure SM 58 microphone. The microphone wasplaced in the axis of the bell (distance = 10 cm)a nd connected to apreamplifier Canford 20-266 and aD igigram Vx pocket V2 soundcard. The musician wasasked to play amezzo forte (mf )d ynamics. Tworepetitions of the note were recorded. In order to limit the variability inherent to the musician as much as possible, he wasasked to play the note in the easiest and most natural way(without trying to adjust the pitch or the timbre of the notes). However,t hese real sounds were too easily distinguishable in asame-different test because of the inherent impossibility of the trumpeter to produce sounds with aconstant envelope, pitch, intensity and spectral content. For this reason, these sounds were resynthesized in permanent regime. The sounds were windowed to suppress the transient part of the signal. With the remaining part (considered as permanent regime), aF ourier analysis (FFT algorithm)w as made to extract the playing frequency F 0 and the magnitude of the harmonics. In practice, only the first 15 harmonics were considered, the amplitudes of the harmonics above the 15th being negligible. In the end, the sounds were resynthesized by additive synthesis, using the playing frequency F 0 and the amplitude of the 15 harmonics (duration = 0.75 s).T wo different recordings were made in order to constitute twodifferent instances of the real trumpet sound providing different sounds to each group of participants. The spectra of the 3trumpets corresponding to the first repetition are presented in Figure 5(right column)for information. This set is called resynthesized real sounds. Frequency normalization: The simulated and recorded sounds of the different trumpets present important differences in terms of playing frequencies. It is however interesting to study howt he discrimination between the instruments changes when these playing frequencyd i ff erences are removed. These frequencyd ifferences are even meaningless for instrument discrimination because one can tune the instrument to get the same pitch (bya djusting the tuning slide). An objective frequencyn ormalization of the sounds is done to overcome this tuning problem and to assess howinstruments are discriminated according to timbral characteristics only.O fc ourse this tuning could be done by the musician, butt his process is subjective,a nd furthermore solves only the problem for the "real sounds" category,n ot for the simulations. At arget frequency wasp referred to normalize the sounds in an objective way. 480 Hz wast he target frequency( corresponding roughly to the average playing frequencyofe very Bb4 sound). All the sounds (simulated -resynthesized real sounds)w ere re-generated by additive synthesis (sum of the contribution of the harmonics)a tafundamental frequencyof480 Hz (duration = 0.75 s).Consequently, twom ore sets of sounds with normalized playing frequencies (480 Hz)w ere added to the twoi nitial sound categories (simulated and resynthesized real sounds). These twonew sets are called respectively normalized simulated sounds and normalized resynthesized real sounds.T osum up, the 2initial sets of sounds are doubled producing 4s ets which are used to assess instrument discrimination).

Dynamics discrimination

The objective of this second experiment is to study whether the differences between the dynamics of the same trumpet are perceptible, the sounds either being played by amusician or simulated. It is important to mention that the effect of the dynamics concerns only the timbre and playing frequencyo ft he notes (sound amplitude is normalized in order to avoid the discrimination linked to sound loudness). According to the dynamics indication used in music, sevenlevels were considered to define the stimuli. Only one instrument is considered in this study: trumpet C (standard leadpipe).

Simulated sounds: Previous studies showed that the main control parameter that governs the dynamics of a simulated sound is the mouth pressure P m (the physical model behavesactually in agreement with areal player) [START_REF] Petiot | Comparison of trumpets' sounds played by am usician or simulated by physical modelling[END_REF]. Seveni ncreasing levels of the mouth pressure P m were chosen to represent the evolution of simulated sounds dynamics (increasing from d1, P m = 8000 Pa, to d7, P m = 20000 Pa by astep of 2000 Pa). The lowest levelc orresponds to the minimum pressure necessary to obtain convergence of the simulation (regime of oscillation), the highest levelcorresponds to the value of the pressure from which there is no spectral enrichment anymore. The other values of the parameters of the virtual musician are chosen in order to obtain convergence of the simulations (f l = 400 Hz, µ l =+ 0 ,66 kg/m 2 ). These sounds are normalized in amplitude to prevent sound intensity difference (see belowthe normalization process). This set is named Simulated sound dynamics. Resynthesized Real sounds: The same resynthesis described above over7gradual dynamics (spanning from pianissimo pp to fortississimo fff)played by the same musician formed al ast set called Resynthesized Real sound dynamics.

It is important to mention that no frequencynormalization wasundertaken for dynamics discrimination because these sounds relate to the same instrument (C).Indeed, frequencyd i ff erences are meaningful to represent dynamics differences (ino ther words, it would be totally irrelevant to change the tuning of an instrument according to the dynamic played).

Experimental design

This section describes the design of the pairs for the same-different test. Regarding instrument discrimination, for each considered set, every pair of instruments is presented as well as the pairs made of the same instrument, which produces 6p airs (A/B, A/C, B/C, A/A, B/B, C/C). Concerning dynamics discrimination, it is useless to compare twod ynamics that are too farf rom each other as the differences are easily audible (pilot tests showed a1 00% success rate in the discrimination). Consequently,t he dynamics discrimination is studied within three ranges R1={d1, d2, d3}(low), R2={d3, d4, d5}(middle), R3={d5, d6, d7}(high), producing pairs as follows: d1/d2, d2/d2, d2/d3. This process produces 9 pairs for each concerned set (Figure 6).F or the instrument discrimination, sets of pairs were heard twice by the participant in order to have an insight into participant answer consistency. Dynamics discrimination pairs . Definition of the pairs presented in the experimental design for the listening test (simulated sounds). On the left, pairs presented to each participant regarding instrument discrimination involving twov irtual musicians, vm1a nd vm2. Pairs were repeated for this test (xxs tands for ap air presented twice). On the right, pairs of sounds regarding dynamics discrimination (x stands for ap air presented). 7d ynamics levels were proposed, from d1 (low dynamics)tod7(high dynamics).

were not repeated so as to shorten the test duration as much as possible in order to keep participant concentration in this difficult test. Figure 6s ummarizes the pairs proposed to the participants regarding instrument discrimination (left)a nd dynamics discrimination (right)i nt he case of simulated sounds. Identical tables can be drawn for resynthesized real sounds. Since the dynamics discrimination does not concern normalized sounds, only the table on the left can be reproduced for normalized synthesized sounds and normalized resynthesized real sounds. Each participant listened to at otal of 66 pairs (2(repetitions) × (6(simu) +6(real) +6(normalized simu) +6(normalized real)) +1(repetition) × (9(simu) +9(real))).B efore the test, ashort training introduced the task. The pairs were presented in ar andom order to each participant to control apossible order effect in the assessment. Furthermore, the sound order in the pair wasalso randomized to prevent an order effect. The participant listened to ap air and provided his/her answer ("same" or "different")i nteracting with the graphical interface (see Figure 4).T he average duration of the test wasabout 20 minutes.

Data analysis

The answers gathered by the same-different tests are analyzed using the confusion matrices [START_REF] Boley | Statistical analysis of abx results using signal detection theory[END_REF]. These 2 × 2m atrices summarize the choices of the participants according to the nature of the pairs presented (see Figure 7). The columns represent the condition ("are the sounds of the presented pair different or the same ?"), and the lines represent the participants' answers. The numbers reported in the different cells comprise individual judgments from participants, including repeated trials for the instrument discrimination conditions. There are as manym atrices as experimental contexts, namely 3i nstrument discriminations (A/B, A/C, B/C) × 4sets (simulated sounds, normalized simulated sounds, resynthesized real sounds, normalized resynthesized sounds)p lus 3d ynamics ranges (R1, R2, R3) × 2s ets (simulated sounds, resynthesized real sounds), giving atotal of 18 confusion matrices. presents an example of the confusion matrix for the instrument B/C comparison in the case of normalized simulated sounds. From these matrices twomain indices assess the quality of the discrimination: sensitivity and specificity [START_REF] Altman | Statistics notes: Diagnostic tests 1: sensitivity and specificity[END_REF]. The first one provides afraction describing the subjects' ability to differentiate sounds and the second one a fraction describing the subjects' ability to detect similar pairs.

Results and Discussion

Discrimination between instruments

The results of the confusion matrices for each experimental condition are represented with ac ommon plot in signal detection theory,t he ROCp lot (ReceiverO perating Characteristic) [START_REF] Mahony | The signal detection theory ROC curve: Some applications in food sensory science[END_REF]. This graphical plot represents the sensitivity (true positive rate)according to (1-specificity), i.e. the false positive rate. Each confusion matrix is represented by apoint in the ROCspace. Aperfect detection is represented by the (0,1)p oint, and arandom detection is located along the diagonal line. The relevance of the discrimination can be assessed on this plot by the distance of the point above the diagonal: the greater this distance, the more relevant the discrimination. To qualify the performance in the detection relatively to random choices in the same-different test, an independence test using the Binomial Distribution is proposed [START_REF] Boley | Statistical analysis of abx results using signal detection theory[END_REF]. From the Binomial Distribution, with as pecificn umber of trials, and an observed number of correct identifications, ap-value can be calculated. This value represents the false rejection probability of the null hypothesis H0: subjects answered randomly to the tests. At hreshold of 5% is considered for the significance test, allowing the computation of threshold values for true positive (TP) and true negative (TN). These thresholds allowt he plotting of a9 5% confidence limits on the ROCspace, for sensitivity (lower bound)and specificity (upper bound). These limits are afirst indicator helping us to estimate the subjects' answers reliability.

Figure 8a nd Figure 9s ummarize the results of the same-different tests and showthe positions of the pairs of sounds in the ROCs pace. The "raw" sounds (simulated sounds and resynthesized real sounds)a re represented in Figure 8, while the normalized sounds (normalized simulated sounds and normalized resynthesized real sounds) are represented in Figure 9.

In Figure 8, for rawsimulated sounds, the sensitivity of the detection is the weakest for the pair B/C (0.32). Furthermore, this pair is belowthe sensitivity confidence limit emphazising howt he participants miss the difference between the 2instruments. We can conclude that the difference between the instrument Band Cisalmost inaudible, while differences for the twoother pairs A/B and A/C are more audible (sensitivity > 0.8)a nd of the same magnitude. Concerning resynthesized real sounds, even if the pair B/C scores higher than its simulated counterpart, its discrimination is not as good as the one of the twoo ther pairs. These observations are in agreement with the amplitude of the differences between the instruments. Indeed, the instruments Ba nd Ch aveav ery similar geometry while Aismore different to Band C. When the difference between instruments is important enough (A/B or A/C), the physical model is then able to generate perceptible differences between the instruments' sounds.

In Figure 9, concerning normalized simulated sounds, the sensitivity is very low: each couple (A/B, A/C, B/C) is under the 95% confidence limit and has as ensitivity inferior to 0.5. When pitch differences are removed, the timbre differences between simulations are too small to be audible. Ye tthe detection for resynthesized real sounds is better (sensitivity > 0.8), every pair being above the two confidence limits and the pairs of instruments are not differentiated in the same waya sr aw sounds. The conclusion is that the physical model is not able to grasp timbre differences between instruments, while resynthesized real sounds are better discriminated. This could also be the result of the substantial difference between the spectra of the twos ets of sounds. Indeed, simulated sounds contain six harmonics while real sounds contain fifteen harmonics leading to ag reater timbre flexibility.T his is the first limit of the physical model. The comparison of Figure 8 and Figure 9i ndicates that the quality of the discrimination is always better when the sounds are not normalized in frequency. This result, in agreement with the expectations, is an indication on the validity of the test. Indeed, the difference between playing frequencies seems to be the main factor driving instrument discrimination, the timbre having al esser importance in this experiment. To visualize the correlation between the sensitivity and the playing frequencydifferences for the different pairs of sounds, as catter plot is shown in Figure 10: the larger the playing frequencydifference, the more sensitive the instrument discrimination. nate than resynthesized real sounds. Globally,only the R1 dynamics range of the resynthesized real sounds is above both confidence limits while all the other points are under the sensitivity confidence limit meaning the omission rates are too high. The position of the dynamics R1(d1d2d3) and R2(d3d4d5)o nt he diagonal line of the ROCs pace indicates that the detection is random for the simulation. Another general conclusion is the difficulty to perceive differences between mezzo piano, mezzo forte (R2={d3, d4, d5})sounds for both sound categories. Regarding the low simulated sounds performance, ap ossible explanation is that the physical model used to simulate sounds does not enrich the sound spectrum enough while increasing dy- namics because it does not takei nto account the preponderant non-linear effect of high dynamics [START_REF] Norman | Player control of "brassiness" at intermediate dynamic levels in brass instruments[END_REF]. According to the confidence limits, the range that is the most discriminated is the pianissimo to mezzo piano resynthesized real sounds R1 with asensitivity better than anyother pair (sensitivity = 0.88). One explanation could be that the spectral enrichment of the trumpet is very important in the lowd ynamics for the 7t o1 5fi rst harmonics since these harmonics are not shared between simulated and resynthesized real sounds.

Discrimination between dynamics

Conclusions

The perceptual same-different test undertaken overs imulated and resynthesized real sounds in permanent regime confirms the capability of the harmonic balance technique to represent coherent differences between instru-ments. Regarding raws ounds, the results showt hat the simulations are able to generate reliable differences between trumpets when the differences between the instruments are sufficient (A/B-A/C). This is in accordance with the results with the "real" musician. When the differences are small (pair B/C), the differences between simulated sounds are not audible whereas theya re with a "real" musician. This can be due to the musician, butadditional tests should be carried out to confirm it (oranartificial mouth should be used [START_REF] Petiot | Comparative analysis of brass wind instruments with an artificial mouth: first results[END_REF]). Ye t, the normalized sounds analysis shows that the physical model synthesizes too small timbre differences givent he leadpipe modifications while the resynthesized real normalized sounds are more discriminable. This shows one limit of the model regarding the very small leadpipe differences. Besides, since the spectra of the resynthesized real sounds contain fifteen harmonics against six for the simulated sounds this may lead to greater timbre differences for the resynthesized real sounds. Concerning the dynamics, the mezzo piano to mezzo forte range is hard to discriminate, particularly for simulated sounds. The protocol selecting recorded sounds for dynamics discrimination should be modified classifying ap riori sounds according to some descriptors related to loudness. The discrimination tasks are difficult for the subjects and the leadpipe modifications were too small to be perceptible in general. Consequently,o ne primary approach would be to refine our geometrical variables in order to objectively produce sharper sound differences. Furthermore, truncating the signal taking only the permanent regime is simplistic and temporal simulation will be studied in the future to provide more perceptually representative simulations. It is worth mentioning that deeper longer tests with manyr epetitions, assessing carefully the musician effect should be undertaken in order to seize more information in anear future.

Figure 1 .

 1 Figure 1. Location of the trumpet leadpipe (inblack)inthe whole trumpet.

Figure 2 .

 2 Figure 2. Dimensions of the parameterized leadpipe and definition of the four radii r 1 , r 2 , r 3 , r 4 .

Figure 3 .

 3 Figure 3. Input Impedance Z (magnitude)o fthe three trumpets A, B, C. The lowest resonance of Z (first regime), not played in the current trumpet tessiture, is not shown on this figure. The fourth peak (corresponding thus to the third maximum), is located around 460 Hz.

Figure 4 .Figure 5 .

 45 Figure 4. Description of the interface for the same-different test.

Figure 6

 6 Figure 6. Definition of the pairs presented in the experimental design for the listening test (simulated sounds). On the left, pairs presented to each participant regarding instrument discrimination involving twov irtual musicians, vm1a nd vm2. Pairs were repeated for this test (xxs tands for ap air presented twice). On the right, pairs of sounds regarding dynamics discrimination (x stands for ap air presented). 7d ynamics levels were proposed, from d1 (low dynamics)tod7(high dynamics).

Figure 7 ConditionFigure 7 .

 77 Figure 7. Example of ac onfusion matrix of the same-different test regarding instrument B/C comparison using normalized simulated sounds. Furthermore, sensitivity and specificity are defined.

Figure 11 Figure 8 .Figure 9 .

 1189 Figure11shows the discrimination between dynamics in the ROCs pace. Simulated sounds are harder to discrimi-

Figure 10 .

 10 Figure 10. Scatter plot of sensitivity according to playing frequencyd i ff erences for the different pairs of sounds (simulated sounds (•)and resynthesized real sounds (•)).

Figure 11 .

 11 Figure 11. Position of triplets of dynamics (R1low dynamics, R3 high dynamics)i nt he ROCs pace for the simulated sounds (•) and the resynthesized real sounds (•).

Table I .

 I Va lues vm1a nd vm2o ft he control parameters for the simulations (virtual musician).

	Definition	Notation vm1	vm2
	Resonance frequencyofthe lips f l (Hz) 400 Mass per area of the lips µ l (kg/m 2 )0 .5 Pressure in the mouth P m (Pa) 16 000 412 0.33 Width of the lips b (mm) 10 Rest value of the opening height H 0 (mm) 0.5 Quality factor of the resonance Q l 5

Acknowledgments

Acknowledgments to the 24 subjects who took part in the perceptual tests with great care and patience.