Pierre Couprie
email: pierre.couprie@paris-sorbonne.fr

EAnalysis : Developing a Sound-Based Music Analytical Tool

Introduction

Analysing electroacoustic music is always difficult. Mostly works do not have visual support or score and when the music has a score, e.g. mixed music, the electronic part is usually written as a form of code and understanding relations between the signs and sound is complex. This is why most musicians use graphic representation to analyse electroacoustic music, to create spatialisation scores, or to transmit knowledge to their students. Also composers use sketches to elaborate forms, structures or memorise their works during the creative process.

Acousmatic music is not representative of current electroacoustic music. A lot of musicians use live electronics, improvisation, other arts -such as video, sound sculpture, poetry, etc.

-where technical means are an important part of the work and recording these performances is very difficult. A stereophonic sound file alone cannot define the work. Many current electroacoustic works are allographics [START_REF] Genette | L'oeuvre de l'art[END_REF], they are defined by different recordings of different performances, multitrack recordings of different instruments/devices, video recordings, scores, data from different devices, and so on. Electroacoustic means and electronic instruments are hybrid and modular. Analysing an electroacoustic performance is a real challenge because you may need to use a range of software to segment sound material, compare various data in different formats, analyse interactions between musicians through movie recordings, and create representations of structures and relations between parts or elements of the performance. Moreover, most software is not compatible, there is no standard exchange format.

Enhancing analytical software is very important but enhancing representation is also essential. To analyse various types of data, we need to create suitable representations: sound representation, line and form/structure charts, graphic representation of units or moments. These representations need also to integrate images or other representations of performance, and even from the creative process itself. Representation in electroacoustic music analysis is not only a graphic representation with beautiful shapes in various colours, each of them representing a sound. Representation can also include sonograms, curve charts of audio descriptors, representation of interaction message lists between musician and computer, tables with time cues, structure representations, space motions, or relations between image and sound in video music.

EAnalysis 1 was created to fill the gaps that exist between various analysis software applications. EAnalysis cannot do everything musicologists, teachers, or musicians want, it is a workspace where the user can create representations, import data from other software or recorded during performance, and analyse them. I did not reinvent the wheel; this piece of software offers the possibility to import data and to export analyses in different formats. It is based on another programme, iAnalyse, which was created for written music analysis. But EAnalysis is very different because the main support of iAnalyse is the score and the main support of EAnalysis is the sound. This chapter presents the development of EAnalysis from three angles. The first is representation and its role in music analysis. The second is new concepts introduced by the software. The final angle presents the most important features of EAnalysis through presenting different examples.

Analysis with graphic representation

Role of graphic representation in musical analysis

Musical analysis generates representations [START_REF] Chouvel | Musical analysis and the representation. 7th European Music Analysis Conference[END_REF], representations of form, structures, relations between various elements, representations with or without time, etc. Musicologists need representations to analyse or to present their analyses. Several theories of analysis are also based on representations such as Schenkerian reduction, paradigmatic segmentation, or various representations of harmony. Representation is important for musical analysis because this is a study of a time art. Humans need to write time down to capture the ephemeral moment and study it. For example a representation of structural segmentation in a formal diagram can reveal new points of view on musical structures. Analytical representation is always a reduction of the musical process. They focus on one or several musical parameters to reveal internal or external relationships between them. In a pedagogical field, analytical representation can also reveal implicit relations or structural processes.

One of the particularities of electroacoustic music is to have no (or to have incomplete) visual support. Mixed music uses a score with various symbols or graphic shapes to represent the electronic part. These symbols can represent a number of preset, simple text indications of sound transformation, or graphic shapes representing a reduction of the electroacoustic part. All of these have great importance for the musician and/or technical assistant. Musicologists and musicians can also use them to analyse the work, to understand the musical ideas of the composer, or to reconstruct the creative process. Teachers can use them to understand the work and to prepare presentations for their students. But these texts, symbols, or graphic reductions are limited to what the composer wants to give you, to what he thinks important to perform his work. These indications are important to analyse the work but they are not in themselves an analysis. Analysing a work means understanding complex relations between parts/moments/units and revealing something difficult to perceive through simple listening. The most important goal of analysis is to give you keys to understand music. Students need these keys to understand how the composer works or to create their own music. Teachers need these keys to present work to their students and to move their ears to what they need to hear. Musicologists need these keys to develop their own theory of music, to create links between different works, or simply to understand aspects of a work. These keys cannot exist only in thought, you have to record them through text, simple graphics, or more elaborated graphic representations. Moreover, these records are also very important for memory. With them, you can memorise, anticipate, link moments of a work even if they are not close, and navigate inside one or several works.

Graphic or text representations are important to study and understand electroacoustic music [START_REF] Couprie | La representación gráfica : una herramienta de análisis y de publicación de la música[END_REF]. But there is also another aspect, the transmission of knowledge: how a teacher can transmit to his students an analysis of electroacoustic music; how a researcher can transmit his analysis or his music theory; how a student can share experiences of listening to a work. For many different reasons, graphic/text representations are a good solution to sharing and publishing analysis. Interactive examples of electroacoustic music created with sonogram/waveforms and graphic/text representations are more efficient at communication than a simple reference to an extract of an audio track.

How to create a graphical representation?

How to create a graphic/text representation depends to what you want to do with [START_REF] Couprie | Re)Presenting Electroacoustic Music[END_REF]. If you need to guide your students inside a work, maybe it is better to use iconic graphics. Links between music properties and iconic graphics are easier to understand. Listeners will not need any explanation or key to associate particular aspects of music and graphic shapes. Figure 1 represents two types of representation: an iconic shape that represents the dynamic part of the sound, a symbolic shape that represents the sound type. I used the second in a representation of a work by Alain Savouret [START_REF] Terrugi | Hétérozygote et les Presque rien[END_REF]. The colour of the shape represented the level of sound transformation and the form of the shape represented the sound type. I decided to use symbolic rather than iconic representations because the structure of the work is very formal, a theme and variations. Demonstrating how the composer used sound transformations to structure his work seemed to be easier using symbolic representation. If you need to communicate complex analysis with a number of different parameters, you need to associate iconic and symbolic representations. The iconic part allows the representation of significant moments or saliences of musical flow. With the symbolic part (text or graphics), you can represent numerous sonic properties, structural layers, musical functions, or very detailed analysis of moments. This takes more time but a key to understand it is a good complement to the iconic part.

The symbolic part also allows the analyst to represent several points of view. Placing side-byside different interpretations of structure or different segmentations of musical flow is a good way to transmit complex relationships or indeterminate aspects of analysis [START_REF] Roy | L'analyse des musiques électroacoustiques : modèles et propositions[END_REF].

One last point I want to make concerns the aesthetic aspect of graphic representation. Do we need to be neutral or do we authorise an aesthetic look to the graphics without links with the music? Once again, this aspect depends on what you want to do with your graphic representation. If representation is only to analyse or is only a part of your research process, you do not need to consider this question. But if you have to communicate your analysis to a range of different people, maybe you have to consider further the communicational aspect of your work. For a paper on Luc Ferrari, I realised graphic representations (Teruggi and [START_REF] Terrugi | Hétérozygote et les Presque rien[END_REF] that are very close to artistic or pedagogical realisations. These representations were an experiment to extend the borders of analysis by representation.

EAnalysis : New concepts for analysis and graphic representation

Applications and limitations of current software

As I have developed in several papers, creating a graphic representation of electroacoustic music is complex. Complex because analysis is complex: you have to determine a point of view, you have to learn the work in depth, you have to extract significant aspects and link them to others in the work or in other works. The process of analysis of electroacoustic music is like discovering a new landscape without knowing the right way forward… and there is no right way. Very often, you have to change direction or to start again in a different direction. Knowing the final direction when you start the analysis is very rare.

Using software to analyse electroacoustic music is important because you need to learn about properties of sounds, to validate your listening or to help your listening when the musical flow is too complex. Maybe it will be useful to mask some sounds or to change the gain of other sounds to understand the different layers of the music. Several software programmes are very useful for this. There are 4 categories:

1.! Software to manipulate audio by filtering, changing gain, or changing pitch:

Audiosculpt2 and SPEAR3 are perfect examples for that. Both of them are analysis/synthesis software. Audiosculpt was developed for composers to sculpt the sound. With SPEAR, you can extract formants and manipulate them individually. These programmes are complex to use but very important for musicologists who want to work on sound. They can extract parts of a complex spectrum and thus focus their analysis on specific sound properties. 2.! Software to extract data from sound: Audiosculpt and Sonic Visualiser4 (with Vamp plug-ins) are good examples. Sonic Visualiser uses the Vamp plug-ins to extract audio descriptors such as spectral centroid, inharmonicity, energy, etc. These descriptors help researchers to isolate individual sound characteristics as clues for musical analysis. 3.! Software to annotate or to create graphic representations: Sonic Visualiser, ASAnnotation5 , MetaScore6 , Acousmographe7 , or Flash/Multimedia sketches.

Creating flash or HTML5 animation is a good option for multimedia publications but this needs coding and complex development. Then, other software such as Acousmographe or MetaScore are good compromises. Unfortunately MetaScore is not publicly available, this software was developed for the library of Cité de la Musique (Paris) and is only used for internal publications. If you only need to annotate, e.g. to add small texts (markers) to a sound, then you also can use Sonic Visualiser or ASAnnotation.

4.! Software oriented musical analysis: Acousmographe with the Aural Sonology Plug-In8 , Acousmoscribe9 , and TIAALS10 . The first two packages contain tools to describe and represent sounds with an augmented version of Pierre Schaeffer's sound object theory [START_REF] Thoresen | Spectromorphological analysis of sound objects: an adaptation of Pierre Schaeffer's typomorphology[END_REF]Di Santo, 2009). TIAALS focuses on sound material analysis and realisation of typological, paradigmatical or other analytical charts.

These categories are of course not limited to these specific software packages. I only presented here the most advanced or useful software to analyse electroacoustic music.

Unfortunately, these software packages have limitations:

•! They cannot analyse audio-visual files, they only use sound files, and most of them only stereophonic files. Video music and multitrack works are very common in electroacoustic music. Moreover, video is a good support to analyse performance. •! Several of them cannot export their data to readable files or import data from other software. There is no format to exchange analysis data between them but nevertheless, analysing electroacoustic music requires the use of several software applications from the extraction of data to creating representations. •! The interface is often limited and not adapted for musical studies. E.g.: there is no possibility to navigate inside a file and to compare different moments of a work or of different works. •! While they have interesting features (such as the Timbre Scope of Acousmographe or drawing of audio descriptor values on the sonogram with Sonic Visualiser), most of them are difficult to use in some contexts (e.g. with a long work, without the possibility to filter data, or to synchronise with a graphic representation, etc.).

To this list of software, I have to add programmes for interactive analysis. Several musicologists have published realisations that are closed software, proposing interactive experiences or musical material for reader. Michael Clarke has published several analyses as standalone software applications [START_REF] Clarke | Analysing Electroacoustic Music: an Interactive Aural Approach[END_REF]. Even if these realisations are not exactly software because the user cannot use them to analyse other pieces, the interactive parts are very complex and seem to consist of small applications to explore the composer's musical researches. In the field of creative process analysis, Ircam has published several CD-ROMs such as those on Philippe Manoury [START_REF] Battier | PMA LIB The electronic music of Philippe Manoury[END_REF] and Roger Reynolds [START_REF] Mcadams | Creation and perception of a contemporary musical work: The Angel of Death by Roger Reynolds[END_REF]. These CD-ROMs contain analysis and musical material from the specific work. Readers can use them to create their own analysis.

This short presentation of the most common software used in analytical research demonstrates that current packages offer a huge array of possibilities to the researcher. Each software application is focused on very specific and powerful features. Unfortunately, most of them were not developed by or with musicologists. They are not the result of the study of musical analysis workflow. Analysing music requires some useful features that these software packages do not integrate.

EAnalysis: Towards a new tool for electroacoustic music analysis

If these limitations were not so important 15 or 20 years ago, they are more problematic to study more recent electroacoustic music. This is why I decided to reverse the method and to develop EAnalysis in a different way:

•! To develop software suitable for musicologists and musicians -while not only for them they are the primary targets. •! Not to reinvent the wheel: e.g. there already exists good software to realise data extraction from sound, so use their results but do not redevelop them. Various limitations of other software had to be resolved with EAnalysis:

•! Projects in EAnalysis would be able to use one or several audio-visual files.

•! EAnalysis would interact with other software through import/export features.

•! The interface would be developed to study sound and music, not only to play a sound file like a very simple player. •! Each feature would be well configured not to be limited to a specific context. This list of goals is the result of several years of research. I have used various software packages in my papers and experimented with them for musical analysis. Unfortunately, musicology rarely integrates digital developments but nevertheless to study recent electroacoustic composition and to go beyond common representation/analysis are very important goals for research.

Inside the development of EAnalysis

From iAnalyse Studio to EAnalysis

The development of EAnalysis was a long process. The project 'New multimedia Tools for Electroacoustic Music Analysis' started in October 2010 but EAnalysis is in part the result of my previous research. Over several years around 2006, I developed a first piece of software, iAnalyse 12 , which was a presentation application for musicians. It contained slides and graphic shapes much like Powerpoint but each of them could be synchronised to an audiovisual file. iAnalyse was perfectly adapted to the presentation of written music. The user could annotate a score, create a playhead to help the following of the score, and create simple animations for musicologists or teachers. Around 2008, I imagined a development of this idea to extend it with analytical tools. In 2008, I presented to the EMS Conference new features that included possibilities to analyse electroacoustic music. Annotations were based on Lasse Thoresen's system [START_REF] Thoresen | Spectromorphological analysis of sound objects: an adaptation of Pierre Schaeffer's typomorphology[END_REF] and were used with a sonogram. This first presentation was very incomplete and worked only as a simulated part of iAnalyse. Then, I started research to create a system of annotation that was more open and that included other analytical theories. Indeed, soundscape analysis [START_REF] Schafer | The soundscape[END_REF], spectromophology [START_REF] Smalley | Spectromorphology: explaining sound-shapes[END_REF], Temporal Semiotic Units [START_REF] Hautbois | Temporal Semiotic Units (TSUs), a very short introduction[END_REF], functions [START_REF] Roy | L'analyse des musiques électroacoustiques : modèles et propositions[END_REF], or language grid [START_REF] Emmerson | The relation of language to materials. The language of electroacoustic music[END_REF] are good examples of what an analytical software package must include. Finally the 'New Multimedia Tools for Electroacoustic Music Analysis' project started and we decided to create a separate piece of software instead to include the electroacoustic analytic tools already inside iAnalyse.

During these years of research, I realised that to create tools for electroacoustic music analysis needs very specific thought and solutions for analysis. Then, I needed to re-think the current tools. I followed 3 main ideas:

•! Analysis of electroacoustic music involves starting with analysis, not with drawing.

Drawing is the final step and it should be possible to automate the mapping between analytic and graphical parameters •! Analysis is a great tool to understand music and concerns not only musicologists. One of the aims of the 'New Multimedia Tools for Electroacoustic Music Analysis' project was to create a toolbox for different types of users. The software must offer a range of strategies adapted to very different types of music, users and habits. •! Analysis means to use and to link various different research and results, the software must be able to import and export data from and to other software. Moreover, users must be able to exchange part of a work, develop libraries or a whole analysis.

Some of these ideas have been realised in EAnalysis as it exists at the time of writing, others have yet to be developed to be more efficient. But research has been started and if EAnalysis is only a laboratory for these ideas, it is a substantial laboratory for future developments.

Associating various points of view

One of the most important goals of EAnalysis is to represent several parameters or values at the same time. In previous research, I demonstrated the difficulty of representing more than 4 analytic parameters in the same representation [START_REF] Couprie | La representación gráfica : una herramienta de análisis y de publicación de la música[END_REF]. Common graphic representation uses X/Y-axes and shapes to represent sound parameters:

•! X-axis usually represents time position and time duration.

•! Y-axis usually represents pitch or a frequency range.

•! Morphology of shape is used to represent amplitude of the sound.

•! The analyst can also use colour and texture to represent frequency range, grain, or structural level.

Figure 2 represents the beginning of a piece by Alain Savouret. I worked on a graphical representation of this piece for the CD-ROM La musique électroacoustique by INA-GRM [START_REF] Couprie | Transformation/transmutation. Analyse d'un extrait de Don Quichotte Corporation d'Alain Savouret. La musique électroacoustique[END_REF] and this new representation is based on it. The space of figure 2 allows the representation of several parameters of sound:

•! X-axis: time position and time duration.

•! Y-axis: panoramic position indicated by letters R, C, L for right, centre and left.

•! The morphology of shape is used to represent type of sound and/or amplitude morphology. •! Colour represents sound transformation: black is original sound, grey is original sound with filter processing, and the light grey ellipse is reverberation.

This graphic representation is very simple but we can observe an important point. Graphic representation is a good tool to represent listening characteristics of sound (type, space position, transformation) and implicit musical aspects (rhythm and duration, structural construction). Moreover, associated graphics, waveform and sonogram allow us to represent more parameters (pitches, range of spectrum, intensity variations). Is it possible to create a complex graphic representation that will associate the information of these three representations?

Adding more parameters demands more dimensions to extend the graphic representation. Use of 3D causes two important problems:

•! The listener misses precision. Distinguishing exact positions between different shapes becomes complex. •! 3D adds only 1 further dimension for 1 parameter: How can we add more parameters?

Another issue is to create different kinds of representations within only one analysis. Musicologists needs to change their point of view without recreating their graphic representation. Current software is limited because analysis is created through drawing: you segment sound material and analyse structure by drawing shapes. Changing point of view or creating another representation with time and frequency positions of shape you have already created demands a redraw, a new representation. This limitation can be removed by disconnecting analysis from drawing. EAnalysis offers the possibility to create analytic events with time and frequency positions. The analytic part of the event consists of several analytic properties that you have created for your analysis. After you analyse, you decide how shapes are drawn. A system of rules, like in style sheets, allows associating analytic properties to graphic properties. Events contain 3 types of properties: bound, that is the global frame properties; graphic, that contain all properties for drawing, and analytic, that are optional properties to list any kind of analytic description of sound. These events are drawn in a time view from bound and graphic properties. But the user has the possibility to change any bound and graphic properties from graphic, or analytic properties.

This system is powerful and allows working with several strategies:

•! Creating a common graphic representation without analytic properties and without rules. •! Creating a common graphic representation with analytic properties and drawing different types of representation, different types of analysis. •! Focussing on analysis by working with analytic properties: drawing simple shapes (e.g. a rectangle), adding analytic properties and deciding after how they will be drawn.

Tools for different types of users

Working with different types of users at different levels is one of aims of the project 'New Multimedia Tools for Electroacoustic Music Analysis'. EAnalysis integrates this possibility in 3 parts: modes, types of view, and types of event.

Modes

EAnalysis integrates 3 modes: normal, add text and drawing. These modes allow the user to create events with different tools. Normal mode is the default mode. The user adds an event by 'drag and drop' from a preformatted list or from his own library to the view. With add text mode, the user enters text during playback and can annotate audio-visual files with words or sentences. Each part of the text is an event and the user can switch to normal mode to change its graphic properties. This mode is realised for analysts who prefer to work with text or for simple annotations of ideas during the first listening. Drawing mode is for users who prefer to draw with mouse, graphic tablet, or interactive whiteboard. This mode is very useful to create very simple annotations on a white page, to highlight a sonogram, to work on a whiteboard while listening with children. Moreover, if the user uses a graphic tablet, pressure is detected and might be used to create artistic drawing like calligraphy.

These three modes were the first features that were developed to respond to various users and were not created as individual elements but as part of a global architecture.

Views

The user can create several types of view. These are used to edit and/or show events, images or other data:

•! Time view is the most important view. The background contains waveform, linear or logarithmic sonogram, layers of sonograms, differential sonogram [START_REF] Chouvel | L'analyse musicale différentielle : principes, représentation et application à l'analyse de l'interprétation[END_REF], image, or colour. The middleground shows imported data from other software such as audio descriptors with curve charts. The foreground shows markers and graphic events. The user creates graphic representations with this view. •! Image view displays slideshows of images. E.g. pictures taken during a performance or a soundwalk can be synchronised with the sound recording. •! Map view is used to create a chart from extracts of audio files. These extracts are represented by sonogram, waveform, events or colour and can be linked with lines like a mind map. with data (audio descriptors), similarity matrix from audio descriptors. 2.! A video view with animated film by Robert Lapoujade [START_REF] Bayle | L'expérience acoustique[END_REF].

Associating different views creates a complex representation to study or present results of an analysis. 1.! The sonogram. 2.! The paradigmatic chart of the opening with 3 units (y-axis) displayed in time (x-axis).

3.! The score of this opening extract.

The chart view allows the creation of any kind of chart from extracts of audio files. Blocks of colour, waveform, sonogram, or graphic events represent these extracts. Blocks can be linked and positioned on a white view. In this example, positions represent units and time, but blocks are movable in any direction. The user can select a block and play the corresponding extract or visualise what block is under the playhead when playing the whole piece. Because visualisation of data is important to extract similarity and singularities for musical analysis, EAnalysis also offers other possibilities to create representations from data. Figure 6 presents five type of graphs (from bottom to top):

1.! A similarity matrix does not show values but similarities between values (black represents similarities and white non-similarities). 2.! Simple chart to represent data in a very simple way. 3.! A BStD chart [START_REF] Malt | Le BStD -Une représentation graphique de la brillance et de l'écart type spectral, comme possible représentation de l'évolution du timbre sonore[END_REF] represents evolution of timbre from three audio descriptors in only one line: spectral centroid (Y), spectral variance (height), and intensity (gradient of colours). 4.! A cloud of points can represent five data (X, Y, size, colour, opacity). EAnalysis uses one or more charts in cloud point to represent data from different tracks to help in comparative analysis. 5.! A hierarchical correlation plot (Collective, 2009) represents correlation between two sets of data from different levels of structure. These four examples demonstrate possibilities in term of analysing, teaching, or communicating with EAnalysis. Different configurations of view can also be saved in the same project.

Events

Because events contain three types of property, they can be used for different strategies and with different levels of complexity:

1.! Graphic events are very simple shapes such as are available in every drawing software application: rectangle, ellipse, text, polygon, image, etc. This level is adapted to first annotations of the piece before analysis, working at listening with children, or creating beautiful graphic representations. 2.! Analytic events are preformatted shapes for analysis. Each event contains a graphic shape and one or more analytic parameters. Working with preformatted analytic events is a good starting point for students to learn musical analysis or specialists to apply existing theories. 3.! Users can also create their own analytic events with personalised analytic parameters. This level is highly flexible allowing the user to adapt representation and analytic segmentation to the analysed work or to a personalised analytical theory.

Figure 7 is 3 extracts of an EAnalysis interface: an example of a selected event and its graphical and analytical properties. Graphical properties contain 3 groups of parameters (graphic, text and advanced) and are very close to graphic software. Analytical properties are key-value pairs of parameters. EAnalysis contains fifteen preformatted analytic parameters (sound objects, spectromorphologies, language grid, space, etc.) and users can add their own parameters and group them into a list and library to share with other users. The interface to edit events and manage their properties is simple and flexible.

Events are also completed with markers. Markers are only time positions with simple graphic properties. They can be used to annotate ideas on first listening, or to mark breaks or structure parts. Events and markers are editable in time view. This is why time view is the default view to visualise, listen, and edit analyses. Other views are to display other data.

4.4. Import, export, share works with communities, and communicate

Import and export data

As explained above, modern software must be able to communicate with other software. Musicologists do not work with only one application, they use different software to prepare audio files, to create representations, or to analyse data with several different procedures.

EAnalysis can import and export data from other software through four categories of files:

1.! Audio-visual file is the root file from which the project is created and a common export format. EAnalysis creates a project from a monophonic or stereophonic audio or video file. The user can also import other audio-visual files to work with multitrack pieces or compare different pieces. 2.! Image file is used to create an image event or slideshow inside image view. As an export file format, image is useful to create a key (with export selected event as image feature) or to export an analysis to images. 3.! Text file is a common format to exchange various types of data. EAnalysis uses this to import a list of time cues (to create markers), time value pairs (to create curves in data view), or graphic representations (from ProTools information sessions or Acousmographe XML export). It can also export lists of events and markers to analyse or use in other software such as Open Music, Max, Excel, etc. 4.! EAnalysis has also 2 types of format: eanalysis project and ealibrary. Both of them allow the user to share analyses with or without media files (if copyright does not allow that) as well as event library including personalised analytic parameters.

The fourth point is very important for the 'New Multimedia Tools for Electroacoustic Music Analysis' project. To share their work or research with other communities is the main activity of the musicologist or musician. With the OREMA web site13 , Michael Gatt aims to enhance sharing works, tools, and to develop theory discussion around musical analysis of electroacoustic music. EAnalysis offers two formats to share projects (with or without media) and theoretical research (analytical event library).

In parallel with file exchange data, current version of EAnalysis can also use the LibXtract plug-in and SuperVP14 to compute audio descriptors and modification of gain. The workflow (export from one application to import in EAnalysis) is reduced to some actions inside EAnalysis that use command line tools to communicate with both technologies. The LibXtract plug-in offers the computation of about forty audio descriptors and SuperVP allows us to transform the gain within spectrum areas drawn with graphical events.

Perspectives

With import/export data, EAnalysis can be defined as a workspace. Because it is difficult to create a real synergy between different software applications, allowing the user to exchange data is essential. It increases research in musicology and the power of each piece of software. The first step of development was to offer a large range of possibilities; the second step will be demonstrating them through the realisation of different examples and increasing them by adding new features.

One part of the perspective of EAnalysis development is to show how to use it with other software such as in figure 6 that uses data from Sonic Visualiser. Visualisation of data is a powerful feature of EAnalysis -any kind of lists that contain time-value pairs of data may be visualised.

The second part of the perspective will be adding new software compatibilities. The list presented in section 3.1 contains common software used in musical analysis but musicologists use also other software such as statistical applications or software used in musical production.

EAnalysis needs to integrate these other applications and maybe new types of view to represent their data. These perspectives are very exciting but also very complex, indeed not possible in several cases, because some software uses a specific format with particular representations. As I mentioned, there does not exist a compatible format to exchange data: only software that use text formats (text, XML, JSON) can currently be used in EAnalysis.

EAnalysis was developed to facilitate adding new types of view. But as discussed in section 4.2, new types of representation have to emerge from needs.

EAnalysis answers to the need for a multipurpose tool for electroacoustic music analysis. Of course, this workspace gives new possibilities by working with many types of data and creating representations with them, but EAnalysis is also a classical piece of software because it works with historical theories of analysis. Musicology needs also to go beyond these simple perspectives. During the development of EAnalysis, some decisions were difficult because I realised that several steps were important but appeared also an outdated method and there was a need to restart and go beyond the original aim. The best example is events. In EAnalysis, events are objects with a border (e.g. time and frequency) but are adapted to specific analytical strategies. A lot of recent electroacoustic music works are very complex in term of media or musical realisation and cannot be analysed with bordered or statical objects. Another example of an EAnalysis limitation is the representation of sound. The software proposes different representations from waveform or sonogram. One of them, the similarity matrix, allows us to research singularities inside spectromorphologies but realisation of the matrix from data of different tracks or different pieces needs to be improved with the dynamic time warping (DTW) algorithm [START_REF] Zattra | ACAME -Analyse comparative automatique de la musique électroacoustique[END_REF]. Finally, some researchers are exploring new forms of analytical representation: the MaMux seminar at Ircam presented some of them 15 .

The emergence of researches in this field is evidence that musicologists need new kinds of representation for complex musical relationships.

Conclusion

This paper presents an account of the development of the EAnalysis software. EAnalysis, as a sound-based music [START_REF] Landy | Understanding the Art of Sound Organization[END_REF] analytical software, is created for the study of music based on sound, not only electroacoustic music but also other non-written music. Choices I made to create two or more possibilities to achieve the same result, or different interface parts for the same feature are going in the same direction: to respond to different types of user and to allow analyse of different genres and categories of music. This chapter has presented theoretical origins and technical choices to propose a software package that is more adapted to musical analysis than other software. As I mentioned, above all other goals, EAnalysis is an experimental laboratory 16 . Realisations by Michael Clarke in the field of aural analysis, research on archive preservations [START_REF] Barkati | GAMELAN : A Knowledge Approach for Digital Audio Production Workflows[END_REF], or new representations of sound (differential sonogram or similarity matrix of sonograms) demonstrate the importance of software development in the analysis of electroacoustic music.

Most of the current graphical representations used for the analysis of electroacoustic music are based on the same paradigm: a 2D representation of time and frequency with some annotations. EAnalysis offers other possibilities but this is probably only a first step in a different direction. In the field of electroacoustic music, analytical researches are in their teenage years. Computer science and multimedia possibilities have been developed significantly in recent years. Musicologists have now more keys to explore new paradigms of representation.

Figure 1 .

 1 Figure 1. Iconic versus symbolic graphic representation.

Figure 2 .

 2 Figure 2. Graphic representation of the beginning of Dulcinea, extract of Don Quichotte Corporation by Alain Savouret.

•!

  Structure view shows linear structures with different representations: linear, formal diagram, arc diagram to display patterns, similarity matrix. •! Video view displays the image of movie files. Views are stacked in a vertical axis. Time position can be synchronised or not. Unsynchronised time allows the comparing of different time positions or different track positions in the same piece, or in different pieces. With EAnalysis, the user can associate different types of view. Figure 3 displays 2 types of view (from bottom to top): 1.! 5 time views: waveform of the whole piece, sonogram, graphic representation, chart

Figure 3 .

 3 Figure 3. L'oiseau moqueur by François Bayle (animated film by Robert Lapoujade).EAnalysis displays different types of view: 5 time views, and video view.

Figure 4

 4 Figure 4 displays another example of complex representation. The piece NoaNoa by Kaija Saariaho for flute and electronics is structured around a root cell of two notes. All other segmented micro-structures can be analysed with a paradigmatic chart. This figure represents 3 views (from bottom to top):

Figure 4 .

 4 Figure 4. Sonogram, paradigmatic chart, and score of beginning of NoaNoa by Kaija Saariaho.

Figure 5

 5 Figure 5 shows different types of structure representations (from bottom to top): 1.! Linear structure shows segmentation in a classical manner but colours can be mapped to time duration or title of units. 2.! Formal diagram highlight novelty and repetitions of units. 3.! Arc diagram represent patterns by linking similar sets of units. 4.! Similarity matrix is computed from titles of units and reveals similarity between different parts of the structure.

Figure 5 .

 5 Figure 5. Different types of structure representations (from bottom to top): linear, formal diagram, arc diagram, similarity matrix.

Figure 3

 3 Figure3uses a chart and similarity matrix to represent data imported from Sonic Visualiser. Because visualisation of data is important to extract similarity and singularities for musical analysis, EAnalysis also offers other possibilities to create representations from data. Figure6presents five type of graphs (from bottom to top):

Figure 6 .

 6 Figure 6. Different type of representation of data (from bottom to top): similarity matrix, simple chart (mirrored line), BStD chart, point cloud chart, hierarchical correlation plot.

Figure 7 .

 7 Figure 7. A selected event (left) and its graphical (centre) and analytical (right) properties.

  •! To develop a useful player for electroacoustic music: to navigate and compare different moments of a work or of different works, to play different tracks of a multitrack work, use audio-visual or image files. •! To create analytic/text/graphic tools for the study of music. Simply to create software with beautiful graphic tools to draw anything you want may not be useful to realise a graphic representation. Musicologist, students, teachers, even children need very specific tools to create a music representation during the time of listening or very quickly after. •! To develop specific analytic tools using analytic tags or an interface to compare analyses. Moreover, analytic tools have to be linked to graphic tools. •! To analyse, we need to present and manipulate various values. This is not always possible with a simple two-dimensional view; we need to use them in different kinds of view to create augmented representations. •! Finally, I wanted to create a laboratory to experiment with new types of representation, and new tools without any limits 11 .

Audiosculpt is developed by Ircam and is available through the Forum: http://forumnet.ircam.fr.

SPEAR is free software developed by Michael Klingbeil: http://www.klingbeil.com/spear/.

Sonic Visualiser is developed by the Centre for Digital Music at Queen Mary University of London: http://www.sonicvisualiser.org. Sonic Visualiser uses Vamp Plug-ins: http://www.vamp-plugins.org.

ASAnnotation is a free software based on Audiosculpt and developed by Ircam: http://recherche.ircam.fr/anasyn/ASAnnotation/.

MetaScore is developed by Olivier Koechlin[START_REF] Koechlin | De l'influence des outils numériques interactifs sur le temps musical[END_REF].

Acousmographe is developed by INA-GRM: http://www.inagrm.com/accueil/outils/acousmographe.

Aural Sonology Plug-in is developed by INA-GRM from Lasse Thoresen's research: http://www.inagrm.com/aural-sonology-plugin-0.

Acousmoscribe is developed by SCRIME from ideas by Jean-Louis Di Santo: http://scrime.labri.fr.

TIAALS is developed by the university of Huddersfield and the Durham university: http://www.hud.ac.uk/research/researchcentres/tacem/.

This is why several of them are not finalised and need further research to be accomplished.

iAnalyse Studio is available as a free software: http://ianalyse.pierrecouprie.fr.

Online Repository for Electroacoustic Music Analysis: http://www.orema.dmu.ac.uk.

SuperVP is a technology developed at Ircam to compute spectrum and time transformation. Audiosculpt is based on SuperVP.


domain: scco shs info

text:
Antje Strauß
email: antje.strauss@uni-konstanz.de

Jean-Luc Schwartz

The syllable in the light of motor skills and neural oscillations

Keywords: Neural oscillations, syllabic rhythm, perceptuo-motor integration, envelope entrainment, temporal prediction

Recent advances in neuroscience have brought a great focus on how the auditory cortex tracks speech at certain time scales corresponding to pre-lexical speech units in order to achieve comprehension. In particular, it has been claimed that it is the syllabic rhythm to which slow neural oscillations in the auditory cortex entrain in order to chunk the speech stream into smaller informational units. However, the terms "syllable" and "rhythm" have been treated quite loosely in the current literature. We revisit classic approaches to show that both concepts do not necessarily have an acoustic or phonetic counterpart, which could be directly extracted by neural processes. We would like to suggest that the syllabic rhythm could emerge at the intersection of acoustic-phonetic and motor knowledge of speech. We furthermore propose that nesting of cortical oscillations might be the key mechanism to understand the timing constraints that lead to the emergence of the syllable.

Introduction

Recent advances in neuroscience have brought a great focus on how the auditory cortex tracks speech at certain time scales corresponding to pre-lexical speech units (phonemes and syllables mainly) in order to achieve comprehension. In particular, it has been claimed that it is the syllabic rhythm to which slow neural oscillations (3-7 Hz) in the auditory cortex entrain in order to chunk the speech stream into smaller informational units [START_REF] Giraud | Cortical oscillations and speech processing: emerging computational principles and operations[END_REF]. However, as we argue in this paper, the terms "syllable" and "rhythm" have been treated quite loosely in the current literature. Both concepts do not necessarily have an acoustic or phonetic counterpart, which could be directly extracted by neural processes. But we would like to suggest here that a syllabic rhythm could emerge by considering also the motor knowledge of speech production that gives important clues on the temporal predictability in connected speech. Therefore, we first elaborate on the complex relationships between linguistic syllables and acoustic/ phonetic information on the one hand and articulatory motor rhythms on the other hand. Then, we outline a framework for how to use these relationships in order to determine the functional role of slow neural oscillations in auditory cortex. Finally, we propose that nesting of cortical oscillations might be the key mechanism to understand the link between acoustic, perceptual, linguistic and motor constraints that lead to the emergence of the syllable.

Defining the syllable and the syllabic rhythm Linguistic, acoustic-phonetic and psycholinguistic approaches to the syllable. The syllable plays a central role in most phonological and psycholinguistic theories of speech recognition. The classical linguistic approach to describe how a syllable is organized is based on the syntagmatic relationships between phonemes (see Fig. 1A). It disassembles the syllable usually into three slots namely onset, nucleus and coda where onset and coda consist of zero, one or several consonants and where the nucleus consists obligatorily of at least one vowel (though some languages like Slovak accept syllabic consonants as the nucleus) [START_REF] Hockett | A manual of phonology[END_REF].

The classical phonetic description of the syllable is based on the waxing and waning of sonority [START_REF] Bloch | Outlines of Linguistic Analysis[END_REF]) that is the relative amount of energy used for producing speech. Thus, it has been hypothesised that within a syllable phonemes are sorted from the least sonorous like stop consonants, followed by fricatives, nasals, liquids, glides to vowels as the most sonorous segments at the nuclear syllable position, and are conversely ordered in the coda if there is one. Whereas some authors confirm the correspondence between sonority fluctuations and syntagmatically defined syllables (as it is depicted in Fig. 1B; [START_REF] Bloomfield | Language[END_REF], others doubt that there is a phonetic reality of the syllable [START_REF] Haugen | The syllable in linguistic description[END_REF] and might concede sonority fluctuations to correspond to simple CV-sequences but not to more complex syllables [START_REF] Bloomfield | Language[END_REF][START_REF] Ohala | The Emergent Syllable[END_REF][START_REF] Cummins | Oscillators and syllables: a cautionary note[END_REF]. In fact, there are many exceptions to the sonority rule in all languages. Very common examples are cases of fricative-plosive-onsets in syllables like /spa/ where /s/ precedes /p/ although /p/ is categorized as less sonorous than /s/. Others (including the recent neuroscientific literature) have tried to link local maxima in the amplitude modulation of the speech envelope to the syllable. Here, monosyllables like /spa/ would again constitute an exception because of their two amplitude peaks separated by the silent period associated with the closure of the /p/. The legitimate existence of the psycholinguistic syllable was originally proven in a pivotal study by [START_REF] Mehler | The syllable's role in speech segmentation[END_REF]. They had shown that phoneme sequences were easier to detect if they correspond to a word's syllabification. In their case, the CV-sequence "ba" was detected faster in "ba-lance" than in "bal-con", whereas the CVC-sequence "bal" was detected faster in "bal-con" than in "ba-lance". They concluded that the syllable holds a central role in the perceptual organization of the speech stream, and declared the syllable as a pivotal unit of speech perception.

However, replications of this effect turned out to be difficult [START_REF] Cutler | The syllable's role in the segmentation of stress languages[END_REF][START_REF] Cutler | A language specific comprehension strategy[END_REF], 1986) and might depend on the rhythmic class of a language (e.g., [START_REF] Cutler | The Roll of the Silly Ball[END_REF]. In the same vein, syllabification is often ambiguous [START_REF] Content | Boundaries versus onsets in syllabic segmentation[END_REF]Treimann & Danis, 1988) and even the existence of ambisyllabic consonants (consonants belonging to two syllables at the same time) has been claimed [START_REF] Kahn | Syllable-based Generalizations in English Phonology[END_REF]. Thus finally, serial models where information is sent in discrete chunks like syllables have been discarded in the psycholinguistic community [START_REF] Cutler | The Roll of the Silly Ball[END_REF]. It has rather been argued that the process of speech segmentation and lexical access is based on statistical processes including syllabic structure as one of several cues operating jointly to enable the listener to achieve adequate word segmentation [START_REF] Norris | The possible word constraint in the segmentation of continuous speech[END_REF][START_REF] Norris | Shortlist B: A Bayesian model of continuous speech recognition[END_REF].

Syllabic rhythm and its perception. Rhythmic analyses of speech have determined an average syllable length of about 200 to 250 ms resulting in a 4 to 5 Hz rhythm [START_REF] Ohala | The Temporal Regulation of Speech[END_REF][START_REF] Chandrasekaran | The natural statistics of audiovisual speech[END_REF]. More detailed efforts to define the syllable rhythm acoustically show quite some variation to this average. For example, in some languages longer (stressed) syllables are frequently surrounded by much shorter (unstressed) syllables. If, however, syllables approach the average duration of 250 ms, the probability increases that neighbouring syllables will be also around the same length, hence creating some sort of isochronicity [START_REF] Greenberg | Speaking in shorthand -A syllable-centric perspective for understanding pronunciation variation[END_REF][START_REF] Greenberg | Temporal properties of spontaneous speech-a syllable-centric perspective[END_REF].

A sensitive measure to characterize syllabic rhythm changes over time in a single utterance have turned out to be difficult. If successful it might finally lead to an objective classification of the world's languages into stress-timed (like English or German), syllable-timed (like French or Italian) or mora-timed (like Japanese) languages [START_REF] Ramus | Correlates of linguistic rhythm in the speech signal[END_REF]. Typically, a ratio is calculated in a given utterance between the percentage of vowel durations and the standard deviation of consonant durations. But although reproducing the traditional classification, it is in fact influenced by the tendency that "stress-timed" languages use more complex consonant clusters compared to "syllable-timed" languages.

Thus, it appears to be less meaningful concerning the rhythmic changes across time and not to be robust against changes in articulation rate. The normalized syllable duration "Yet Another Rhythm Determination" proposed by [START_REF] Wagner | Introducing YARD (Yet Another Rhythm Determination) and re-introducing isochrony to rhythm research[END_REF] might provide a possibility to capture the rhythmic irregularities within an utterance that accounts for the greater variability across adjacent syllables in stress-timed languages.

Despite the problem how to quantify the syllabic rhythm, another branch of research asks what the basis of speech rhythm perception is. Some authors showed that amplitude modulations at ~4 Hz but not at faster rates contribute to speech rhythm perception [START_REF] Leong | A role for amplitude modulation phase relationships in speech rhythm perception[END_REF]. But it might be that listeners do not consider syllable onsets for their rhythm judgments but rather the distances between p-centres of syllables [START_REF] Morton | Perceptual Centers (P-centers)[END_REF][START_REF] Marcus | Acoustic determinants of perceptualcenter (P-center) location[END_REF][START_REF] Scott | The point of P-centres[END_REF]. The p-centre (i.e., the perceptual centre) is defined as the "psychological moment of occurrence". The precise physical correlate of p-centres, though, is still under debate and most likely describes the conjunction between multiple (e.g., acoustic and articulatory) cues [START_REF] Dejong | The correlation of P-center adjustments with articulatory and acoustic events[END_REF][START_REF] Patel | The acoustics and kinematics of regularly timed speech: A database and method for the study of the p-center problem[END_REF].

The speech motor syllable and motor knowledge of syllable timing. The syllable is considered to play a central role in speech production as well. Speech errors like slips-of-the-tongue led [START_REF] Crompton | Syllables and segments in speech production[END_REF] to postulate the existence of a mental syllabary, which was later seized by [START_REF] Levelt | Models of word production[END_REF] as how phonological commands are translated into motor gestures.

In terms of motor activity, the syllable has been described as the relative opening and closing of the vocal tract (corresponding to sonority fluctuations; [START_REF] Goldsmith | Autosegmental and metrical phonology[END_REF]. The motor syllable holds a central role in the Frame -Content Theory (MacNeilage, 1998;[START_REF] Macneilage | On the origin of internal structure of word forms[END_REF] where it is seen as the "frame" allowing further phonological "content". It is at around 7 months of age that infants start to babble by alternating between close and open mouth configurations [START_REF] Oller | The emergence of speech sounds in infancy[END_REF], the so-called "closants" and "vocants" [START_REF] Martin | Voice, speech and language in the child: Development and disorder[END_REF]. While indeed vocalizations precede articulation in Human development as well as in Humanoid evolution [START_REF] Oller | The Origins in Syllabification in Human Infancy and in Human Evolution[END_REF], speech similar utterances begin as soon as infants are able to couple vocalizations with orofacial gestures that is when they start to babble. At this first stage of development, utterances are thus dominated by the cyclic movements of the mandible [START_REF] Green | The Physiologic Development of Speech Motor Control: Lip and Jaw Coordination[END_REF]. The development of independent phonemic content inside the frame would only occur at a later stage.

The phylogenetical component of the Frame-Content Theory further hypothesizes that syllables evolved from mastication and other ingestive gestures. Indeed, jaw cycles in speech, mouth openings and syllable rate are highly correlated [START_REF] Chandrasekaran | The natural statistics of audiovisual speech[END_REF]. However, jaw cycles in chewing are much slower in frequency [START_REF] Hiiemae | Hyoid and tongue surface movements in speaking and eating[END_REF]. This is one argument to favour the faster, communicative lip smacking as observed in monkeys as the precursor of the syllable [START_REF] Ghazanfar | Cineradiography of monkey lipsmacking reveals the putative origins of speech dynamics[END_REF].

Whatever the view on development and phylogeny, it is true that in adult speech, the syllable involves complex sequences of articulatory gestures in which the jaw is only, at best, a carrier articulator. Among the concurrent perspectives on the motor structure of syllables, one of the most sophisticated models is provided by articulatory phonology [START_REF] Browman | Towards an articulatory phonology[END_REF], which tries to explain phonological structures by combinatorial constraints of articulatory gestures. The articulatory coordination between consonants and vowels within a syllable is framed as coupled and decoupled oscillators.

That means, consonants at the onset are produced "in phase" with vowels at the nucleus because their articulation is prepared in parallel whereas the production of the nuclear vowels and consonants at the coda are somewhat decoupled [START_REF] Browman | Some notes on syllable structure in Articulatory Phonology[END_REF][START_REF] Goldstein | Coupled oscillator planning model of speech timing and syllable structure[END_REF]. Interestingly, it is furthermore suggested that the temporal alignment of onset consonants and nuclear vowels is constant. Hence especially when the syllable onset consists of a complex consonant cluster, articulation is supposed to be programmed around a "c-centre" (i.e., the consonantal centre), a sort of mean of consonantal targets in the onset, in order to keep a constant temporal distance to the vowel centre.

In summary, the coupled articulatory coordination at syllable onsets might have neural correlates of coupled oscillations in motor cortex. Together with the quasi-regular oscillation of the jaw, they might form a good basis for motor knowledge of syllable timing, which might be also of use during speech perception [START_REF] Wilson | An oscillator model of the timing of turn-taking[END_REF][START_REF] Wilson | Listening to speech activates motor areas involved in speech production[END_REF] especially in adverse listening conditions, as we will outline in the next section.

The emergence of the syllable at the linguistic, acoustic and motor intersection As we have seen, the definition of the syllable and its role in speech comprehension remains challenging for linguists, psycholinguists and phoneticians. In neurolinguistics, the syllable has not played an important role up to very recently. Some researchers have proposed modular processes [START_REF] Friederici | Towards a neural basis of auditory sentence processing[END_REF] and others cascaded processes [START_REF] Hagoort | The fractionation of spoken language understanding by measuring electrical and magnetic brain signals[END_REF] to achieve lexical access without being too detailed about pre-lexical units like syllables. An interesting perspective has arrived based on neural oscillations, which sheds a new (syllable-based) light onto this debate. Several studies have by now shown that slow neural oscillations in the auditory cortex track the envelope of the speech signal [START_REF] Ahissar | Speech comprehension is correlated with temporal response patterns recorded from auditory cortex[END_REF][START_REF] Luo | Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex[END_REF]; for a review see [START_REF] Ding | Cortical Entrainment to Continuous Speech: Functional Roles and Interpretations[END_REF]. The suggested mechanism behind is that intrinsic neural oscillations in the theta frequency band (3-7 Hz) would adapt in frequency to extrinsic acoustic rhythms at a syllabic rate (~4 Hz; Ghitza, 2011) so that microstates of high cortical excitability are aligned with the most informative parts of the speech signal [START_REF] Giraud | Cortical oscillations and speech processing: emerging computational principles and operations[END_REF]). As we have outlined above, the relationship of energy fluctuations, which constitute the speech envelope to a great extent, and syllable structure is not as straight forward. The same holds true for the relationship between envelope and rhythmic perception. Nevertheless, envelope entrainment has often been referred to as tracking the syllable rhythm in the neuroscientific literature, but we are far from having a complete picture of the theta-mediated cognitive mechanisms and in particular of its relation to the syllable. In the following, we will first elaborate on the role of theta oscillations when decoding the speech signal acoustically, and second, we provide new ideas about how theta oscillations may be the common ground of how motor rhythms interact with acoustic decoding.

Theta oscillations in primary auditory cortex and pre-lexical abstraction. As outlined above, current psycholinguistic models on word recognition postulate the existence of pre-lexical units for the efficient mapping of sound onto meaning. In neuroscience, efforts to localize the representation of pre-lexical units have been made and suggest superior temporal sulcus and gyrus as the main loci (for review see [START_REF] Obleser | Pre-lexical abstraction of speech in the auditory cortex[END_REF]. However, the nature of these pre-lexical units remains unclear. In all psycholinguistic models, the prelexical abstraction process has been simplified for the sake of modelling higher-level aspects of the word recognition process (for review see [START_REF] Scharenborg | How should a speech recognizer work?[END_REF]. In Shortlist B, for example, ready-made phonemes are given as an input to the model, and constitute the basis for calculating higher-level probabilities of upcoming phonemes and of the final word segmentation using Bayesian principles.

Interestingly, the recent neurophysiological model by [START_REF] Giraud | Cortical oscillations and speech processing: emerging computational principles and operations[END_REF] might provide a way, how abstract pre-lexical, that is phonemic and syllabic, chunks are formed in the first place. They propose that gamma oscillations in primary auditory cortex decode acoustic-phonetic information in a spatio-temporal manner. This gamma code, however, needs to be integrated similar to second level statistics, which is according to them provided by oscillations in the theta frequency range. Furthermore, gamma-theta coupling might be implemented across different cortical layers of primary auditory cortex. Most importantly, theta oscillations hence mark a window of temporal integration that resembles in its duration linguistically defined syllables.

On the basis of this temporal resemblance, some authors have made strong deterministic claims about how theta oscillations chunk speech streams into syllable-sized units [START_REF] Ghitza | Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm[END_REF]. The functional role of theta oscillations, however, might neither be syllabification nor speech segmentation. In contrast to the claims raised by linguistic approaches to find a final definition of the syllable (by its syntagmatic structure and its sonority fluctuations), the neurophysiological model of gamma-theta coupling instead might provide a less deterministic and more probabilistic approach to the syllable. Here, theta oscillations might only modulate the pre-lexical abstraction process but not chunk the speech stream across time. This notion is supported by recent results showing that theta phase influences phonemic categorization [START_REF] Oevers | Oscillatory phase shapes syllable perception[END_REF][START_REF] Oevers | A 7T fMRI study investigating the influence of oscillatory phase on syllable representations[END_REF], whereas its role in speech segmentation could not yet be established [START_REF] Kösem | High frequency neural activity predicts word parsing in ambiguous speech streams[END_REF]Strauß et al., in prep.). This would mean that the acoustic information is decoded in a phase-dependent manner in order to form abstract phonemic representations, but this might be important to ensure a robust representation against noise and hence is unrelated to speech segmentation [START_REF] Kayser | Spike-Phase Coding Boosts and Stabilizes Information Carried by Spatial and Temporal Spike Patterns[END_REF]. Such a probabilistic modulation would furthermore be in line with the current opinion in psycholinguistics, where discrete syllabic chunks have been discarded in favour of a cascaded accumulation of information [START_REF] Cutler | The Roll of the Silly Ball[END_REF].

Theta oscillations and the missing link to motor cortex. Current models on the functional role of theta oscillations focus primarily on the parsing of the speech signal [START_REF] Giraud | Cortical oscillations and speech processing: emerging computational principles and operations[END_REF][START_REF] Ghitza | The theta-syllable: a unit of speech information defined by cortical function[END_REF]. However, multisensory influences like visual input have been shown recently to modify speech envelope tracking and in particular theta oscillations in primary auditory cortex [START_REF] Crosse | Congruent visual speech enhances cortical entrainment to continuous auditory speech in noise-free conditions[END_REF][START_REF] Zion Golumbic | Visual input enhances selective speech envelope tracking in auditory cortex at a "cocktail party[END_REF]; for review see [START_REF] Peelle | Prediction and constraint in audiovisual speech perception[END_REF]. In the light of the current approach, it is noteworthy that fluctuations in the speech envelope or in energy are also associated with the opening of articulators, in particular with the jaw, to produce vowels and the closing of articulators to produce consonants. Thus, if we pose the evolutionary question whether spoken language tries to match intrinsic cortical oscillations (or the other way round), we inevitably need to answer the question whether there is a specific relationship of the auditory theta rhythm to motor cortex and to articulatory skills.

In order to study this specific relationship, we suggest a tentative schema about potential interactions following from the previous elaborations (see Figure 2). In short, we propose that syllables emerge from a complex set of interactions that comprise not only speech acoustics and slow neural oscillations but also motor rhythms. Concerning the first link, namely the relationship between slow neural oscillations in auditory cortex and the speech envelope, we have already reviewed several studies above that have targeted this question. Some might say that the speech envelope drives theta oscillations in primary auditory cortex whereas others would say that theta oscillations track speech envelope fluctuations (hence rather solid lines for both processes in Fig. 2).

Secondly, we depict (also with a solid line in Fig. 2) the link between articulatory movements that produce the speech envelope as discussed in previous sections. Motor rhythms might encompass simple jaw oscillations up to sophisticated coupling between jaw movements and all the other orofacial articulators in order to produce "the smallest unit of speech utterances", that is the syllable [START_REF] Ladefoged | A course in phonetics[END_REF].

Links that are more questionable and programmatic are drawn in dotted lines in Figure 2.

On the one hand, there is the most important question whether slow neural oscillations constrain articulatory rhythms. In their Frame- Content theory, MacNeilage & Davis (2000) suggested that jaw rhythms provided the basis for speech rhythms. Hence, natural rhythms of the speech envelope would be constrained by natural motor rhythms. However, the current neuroscientific framework suggests that the speech envelope accommodates theta oscillations pre-existing in auditory cortex. Theta oscillations might therefore also provide a "neural temporal frame" for motor oscillations (e.g. [START_REF] Giraud | Endogenous cortical rhythms determine cerebral specialization for speech perception and production[END_REF][START_REF] Morillon | Neurophysiological origin of human brain asymmetry for speech and language[END_REF]. Considering that jaw oscillations in e.g. chewing seem to be intrinsically slower than speech rhythms [START_REF] Ghazanfar | Cineradiography of monkey lipsmacking reveals the putative origins of speech dynamics[END_REF], it could be argued that the theta frame indeed induced a speeding of jaw rhythms aiming at better fitting with the auditory decoding capacities of the human brain [START_REF] Ghitza | Linking speech perception and neurophysiology: speech decoding guided by cascaded oscillators locked to the input rhythm[END_REF].

On the other hand, a considerable number of studies have shown that motor cortices probably contribute to auditory perception particularly in adverse conditions (e.g. [START_REF] Meister | The essential role of premotor cortex in speech perception[END_REF][START_REF] D'ausilio | The motor somatotopy of speech perception[END_REF][START_REF] Sato | A mediating role of the premotor cortex in phoneme segmentation[END_REF]; for meta-analyses and reviews see [START_REF] Scott | A little more conversation, a little less action-candidate roles for the motor cortex in speech perception[END_REF][START_REF] Adank | The neural bases of speech comprehension and speech production: Two activation Likelihood Estimation (ALE) meta-analyses[END_REF]. Its role for speech processing is still open, but some evidence suggests that the motor cortex might monitor rhythm and rate in auditory signals (probably associated with articulatory rhythms) in order to improve temporal predictions of auditory targets [START_REF] Arnal | Delta-Beta Coupled Oscillations Underlie Temporal Prediction Accuracy[END_REF][START_REF] Morillon | Motor contributions to the temporal precision of auditory attention[END_REF]. Interestingly, first evidence shows that the motorrelated beta rhythm is used for predictive timing of auditory events [START_REF] Fujioka | Internalized timing of isochronous sounds is represented in neuromagnetic β oscillations[END_REF]; for review see [START_REF] Arnal | Predicting "when" using the motor system's beta-band oscillations[END_REF]. Hence, slow oscillations in auditory cortex might couple with beta oscillations in order to increase the accuracy of temporal predictions [START_REF] Arnal | Delta-Beta Coupled Oscillations Underlie Temporal Prediction Accuracy[END_REF]. This "when"-path has been suggested to be implemented in the dorsal stream [START_REF] Rauschecker | Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing[END_REF][START_REF] Rauschecker | An expanded role for the dorsal auditory pathway in sensorimotor integration and control[END_REF]. If such auditory-motor coupling could be established, one could further ask whether reported visual enhancement of speech envelope tracking is actually mediated by motor cortices (as there is first evidence pointing into this direction by [START_REF] Park | Lip movements entrain the observers' lowfrequency brain oscillations to facilitate speech intelligibility[END_REF]. This would be in line with some aspects of the classic motor theory of speech perception [START_REF] Liberman | The motor theory of speech perception revised[END_REF]. In any case in speech, such temporal predictions would be indeed helpful in order to anticipate suprasegmental time windows of information integration like phrase boundaries [START_REF] Ding | Cortical tracking of hierarchical linguistic structures in connected speech[END_REF] or to arrive at an efficient turn taking in a conversation [START_REF] Wilson | An oscillator model of the timing of turn-taking[END_REF][START_REF] Scott | A little more conversation, a little less action-candidate roles for the motor cortex in speech perception[END_REF].

In sum with our model, we postulate intimate connections between auditory (and possible visual), motor and neural processes inside the "syllabic" temporal frame. The tracking of acoustic fluctuations over time could be optimized thanks to the recruitment of articulatory knowledge, e.g. via the dorsal stream, especially in adverse listening conditions. This would constitute an efficient system for integrating information in time even under degraded conditions of communication.

Conclusion

It is now becoming increasingly clear that speech communication involves a set of tight relationships between the perceptual and the motor system, in both speech production [START_REF] Guenther | Cortical interactions underlying the production of speech sounds[END_REF][START_REF] Perrier | Control and representations in speech production[END_REF] and speech perception [START_REF] Schwartz | The Perception-for-Action-Control Theory (PACT): A perceptuo-motor theory of speech perception[END_REF][START_REF] Skipper | Hearing lips and seeing voices: how cortical areas supporting speech production mediate audiovisual speech perception[END_REF]. However, computational models for integrating motor and perceptual processes into one cognitive speech communication systems remain rather formal and far from detailed neural implementation [START_REF] Guenther | A theoretical investigation of reference frames for the planning of speech movements[END_REF][START_REF] Moulin-Frier | Adverse conditions improve distinguishability of auditory, motor, and perceptuo-motor theories of speech perception: An exploratory Bayesian modelling study[END_REF]. In this paper, we suggest that neural oscillations could provide a natural framework for perceptuo-motor integration in the emergence of syllabic units in the listener's brain. This opens a large number of experimental questions and theoretical perspectives for both speech perception, speech production and the description of the pre-lexical units of spoken languages.

Figure 1 :

 1 Figure 1: A. Syllable structure in the classical linguistic literature. B. Syllable structure as an acoustic-phonetic sequence of increasing-decreasing sonority. C = consonant, V = vowel.

Figure 2 :

 2 Figure 2: Schema of putative relationships between acoustic rhythms, neural oscillations and motor knowledge letting syllables emerge from this set of interactions. Solid arrows mark relationships that are reasonably well accepted; dotted arrows mark relationships that could be object of future studies and developments in the field.

Acknowledgements

We are grateful to two anonymous reviewers, whose critical input substantially improved the quality of the manuscript.

Funding

The research leading to these results has received funding from the European Research Council under the European Community's Seventh Framework Program (FP7/2007-2013 Grant Agreement no. 339152, "Speech Unit(e)s", J.-L. Schwartz PI).