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\begin{document}
    \section*{Descriptor driven concatenative synthesis tool for Python}
    Sam Perry\\
    E-mail: \href{mailto:u1265119@unimail.hud.ac.uk}{u1265119@unimail.hud.ac.uk}
    \section*{Abstract} 
    A command-line tool and Python framework is proposed for the exploration of
    a new form of audio synthesis known as `concatenative synthesis', a form
    of synthesis that uses perceptual audio analyses to arrange small segments
    of audio based on their characteristics.  The tool is designed to
    synthesise representations of an input target sound using a source database
    of sounds. This involves the segmentation and analysis of both the input
    sound and database, the matching of input segments to their closest segment
    from the database, and the re-synthesis of the closest matches to produce
    the final result.\\
    The  project aims to provide a tool capable of generating high-quality
    sonic representations of an input, to present a variety of examples that
    demonstrated the breadth of possibilities that this style of synthesis has
    to offer and to provide a robust framework on which concatenative synthesis
    projects can be developed easily. The purpose of this project was primarily
    to highlight the potential for further development in the area of
    concatenative synthesis, and to provide a simple and intuitive tool that
    could be used by composers for sound design and experimentation. The
    breadth of possibilities for creating new sounds offered by this method of
    synthesis makes it ideal for digital sound design and electroacoustic
    composition.\\
    Results demonstrate the wide variety of sounds that can be produced using
    this method of synthesis. A number of technical issues are outlined that
    impeded the overall quality of results and efficiency of the software.
    However, the project clearly demonstrates the strong potential for this
    type of synthesis to be used for creative purposes.
    \begin{keywords}
        Concatenative synthesis; Python; audio descriptor; audio analysis; command line tool; Python framework; Python sound;
    \end{keywords}

    \section*{Acknowledgments}
    I would like to thank A Harker for his advice and guidance as a mentor
    throughout the project, and A Harker and P Chen for access to their
    vocal samples database.  Thanks also to D Chaplin for his creative input
    in generating results.
    \pagebreak
    
    \section*{Background}
    The concept of constructing a new sound by arranging collections of smaller
    sounds has gained popularity in the past 30 years through the introduction
    of granular synthesis, which works on the theory that any sound can be
    described through the arrangement of smaller samples (referred to as
    `grains'). This representation of sound allows for the temporal
    decomposition and re-arranging of real-world samples, with the potential to
    create new `complex, dynamically-evolving
    sounds'~\parencite[p.1]{Roads1988}.\\

    Concatenative synthesis (CS) is a form of synthesis that has developed
    significantly over the past 15 years, driven by recent advancements in
    technology. The key advancements have been in ease of access to large databases of
    audio and the development of methods for extracting useful information from
    these databases automatically~\parencite[p. 1]{Schwarz2006a}.  CS utilises
    these technologies to provide a content-based extension to granular
    synthesis; analysis of a database of source grains enable them to be 
    differentiated based on their characteristics.  These characteristics can
    then be used for grain selection in the process of synthesising output for
    a wide range of applications~\parencite[p. 102]{Schwarz2007}.

    \section*{Related works}
    A number of programs utilise CS to achieve various goals. The process has
    been used for applications in areas such as speech synthesis, instrument
    synthesis and creative sound design.\\
    The wide range of applications demonstrates the versatility of this
    synthesis technique. It differs from traditional synthesis methods as it
    uses real recorded samples, as opposed to traditional methods that focus on
    defining sets of rules for emulating real sounds. By transforming samples
    that have been directly recorded from a source, the subtle nuances of the
    source's sound are preserved. These would be difficult to reproduce using
    other synthetic methods for modelling an
    instrument~\parencite[p. 24]{Maestre2009}.

    \subsection*{Speech synthesis}
    Creating a natural and intelligible realisation is an important factor when
    developing a speech-synthesis system. The Talkapillar project is one such
    example of how highly convincing results are possible with CS. Through
    careful analysis of a vocal database, the project aims to impose the
    qualities of the database voice on an input voice. This would result in the
    words of the input speaker being transformed to appear as if they were
    spoken by the voice in the database.~\parencite{Hueber}
    
    \subsection*{Instrument Synthesis}
    Progress has also been made in improving the quality of instrumental
    synthesis. As with speech synthesis, the use of samples directly allows for
    natural-sounding results, which provides a method for reproducing real
    instruments convincingly. Another important aspect of instrumental synthesis is that of performer
    expression. The reproduction of performance qualities such as dynamics,
    timbre and timing is essential when emulating a real instrument and CS has
    been used to effectively reproduce these aspects. This is achieved through
    splicing of grains based on their expressive characteristics to form
    musical phrases.  For example, just as a violinist might transition
    seamlessly from one articulation to the next, the CS software will join
    grains to produce the variation in articulations. This contrasts with the
    traditional approach to sampling, where samples are played in isolation,
    resulting in a discontinuity between adjacent samples~\parencite[p. 82]{Lindemann2007}. 
    The Catapillar project is one such example of this use of CS. 
    By using a Viterbi algorithm, the project is able to calculate the
    smoothest overall transition between grains across the output, resulting
    in convincing synthesis of orchestral instrument performances~\parencite[p. 5]{Schwarz2003}.

    \subsection*{Creative sound design}
    The flexibility of CS allows for creativity in a broader context than simply
    emulating real-world instruments and speech. It can also be used as a tool
    to explore the possibilities for synthesising new abstract sounds for
    creative purposes.\\
    A prominent project in this area of CS is IRCAM's CataRT
    project~\parencite{Schwarz2006a}. The project focuses on the playback of
    source grains based on their proximity to a target in multi-dimensional
    descriptor space. Providing a target point in the descriptor space enable the
    user to navigate the database, playing selections of samples that
    are nearest to the target. This allows the user to explore the database
    intuitively through a graphic user interface, selecting a point in
    2-dimensional space with the mouse. Grains are then played back in
    real-time to create an `audio mosaic'.\\
    Alternatively, target audio can be provided and analysed to create a target
    location based on it's location in the descriptor space.  Tremblay and
    Schwarz's~\citeyearpar{Tremblay2010} use of CataRT to explore
    electroacoustic sample banks demonstrates the creative potential of this
    method. CS is used in this context as a means of synthesising matches in a
    corpus database to real-time input from an electric bass.  Significance is
    placed on linking the playback of grains to the expressively of the
    performer. The use of perceptually based audio descriptors to match the
    source to the target allows the performer to navigate the database
    naturally based on factors such as the pitch and timbre of the bass
    guitar. The result is a performance that mixes characteristics of both the
    bass guitar output and the qualities of the corpus database to create a
    hybrid of the two.\\
    This is by no means an exhaustive overview of the projects and techniques
    that explore the vast possibilities of CS. Further information can be found
    in the article by~\parencite{Schwarz2006b}
    \pagebreak

    \section*{Concatenator}
    The Concatenator project aims to provide an open source tool that allows
    composers to generate a variety of CS driven realisations for sound design
    purposes.  In addition, the project aims to provide an intuitive API that
    Python programmers might use as the fundamental building blocks on which to
    build further CS applications.  The result is a framework and command-line
    interface, built in Python, for easy access to basic CS techniques. All
    relevant material including source code, results, and documentation can be
    found in the official online project repository~\parencite{perry2016a}.
    The current implementation can be used for the concatenation of a source
    database onto target audio files, using a range of perceptual audio
    descriptors for matching.  Database management, simple matching and
    synthesis algorithms are used to achieve this, and are described in the
    following sections. \\

    The features and uses of this tool are most comparable to those of the
    MATConcat project~\parencite{sturm2004}, which was developed to provide an
    open source tool for generating similar representations of audio in MATLAB.
    Although there are technical differences such as the number of descriptors
    available for each project, both share a similar focus on the
    electro-acoustic compositional applications of CS. Results produced for the
    MATConcat project are comparable to those of the Concatenator project, and
    both work offline to produce results. The Concatenator project builds on
    this by providing a wider variety of descriptors and the ability to
    artificially enhance matches (as discussed in the~\hyperref[sat]{Synthesis
    and Transformations section}).

    \section*{Program design and implementation}
    The Concatenator project consists of a number of components that work
    together to produce the final output. A complete description of all
    components and there usage in the Concatenator project can be found in it's
    documentation.\\

    Output is generated by analysing overlapping segments of audio (known as
    grains) from both the target sound and the source database, then searching
    for the closest matching grain in the source database to the target sound.
    Finally, the output is generated by applying a hanning window and
    overlap-adding the best matches. Each component is discussed in detail
    in the following sections.\\

    When designing the Concatenator framework, ease of development, use and
    extensibility were primary considerations. It was for these reasons that
    the framework was written in the Python programming language. Python has
    grown in popularity in the scientific community recently, primarily due to
    its focus on productivity, readability and the large number of efficient
    numeric processing libraries available (\cite{Pedregosa2011,
    Fangohr2014, Scipy}). This makes Python a good choice for
    quickly developing ideas in the context of audio signal processing.
    Unfortunately, the language does sacrifice processing speed for simplicity, 
    and as a result, is not suitable for real-time signal processing. Other
    performance-focused languages such as C++ are better suited to this type of
    processing. However, it was decided that the increase in productivity, lack
    of prior CS research in Python and the author's previous experience made
    it the most suitable choice for this project.\\

    The choice to limit the project to offline processing has both positive and
    negative implications for the function of the project. A key disadvantage
    of this type of processing is the lack of possibility for any live
    performance aspect. This method provides no way of exploring the feedback
    between performer and system in a live environment, as in the work
    of Tremblay and Schwarz~\citeyearpar{Tremblay2010}.
    However, there are advantages to offline processing that would not be
    possible in a real-time context.\\
    One significant advantage is that databases can afford to be far larger
    than they could be in real time. Without the requirement to process output in
    a short period of time, more time can be taken to search vast databases in
    the hope that the closest match to a target will be found.\\
    Another advantage is in the global view of a target that can be taken in an
    offline approach. Because the complete audio file is available from the
    start of processing, techniques can be applied that consider the output as
    a whole, rather than on a grain-by-grain basis. This allows for algorithms
    such as the Viterbi algorithm to find the sequence of grains that provide
    the best continuity, as demonstrated in the Catapillar
    project~\parencite[p. 4]{Schwarz2003} This would not be possible in
    real-time, as audio is processed 'on the fly'.\\

    An additional consideration was the method to be used for controlling the
    target to which the grains would be matched. It was decided that the most
    interesting results would be produced through the matching of grains to a
    target audio file, as opposed to other approaches such as matching to MIDI
    scores. In this sense the project is a form of offline audio-mosaicking
    tool similar to that of CataRT.
    
    \subsection*{Descriptor Implementation}
    In order to differentiate between grains, a number of audio descriptors
    were implemented. Audio descriptors are used to measure a specific
    characteristic of a signal~\parencite[p. 31]{Lerch2012}. For example, a
    root mean square (RMS) descriptor was implemented to give an indication of
    the overall intensity of the grain. Another example is the fundamental
    frequency (F0) descriptor, which was implemented to give a value relating
    to pitch for harmonic grains. These values could then be used by the
    matching algorithm in order to find the best match between the source and
    target grains.\\
    Owing to time constraints on the project, only a limited number of basic
    descriptors were implemented. For this reason, the project was designed so that new
    descriptors could easily be added. The object-oriented
    design of the descriptors provides the potential for quick development of
    any future descriptors to be added. 

    \subsection*{Database design}
    When generating descriptors for large databases, large amounts of data are
    produced and so an efficient method of storing and retrieving the data was
    needed in order to manage this. The Python interface to the HDF5
    filesystem~\parencite{Collette2016} was chosen for it's simplicity and
    ability to compress the data automatically. Storing Numpy arrays of
    descriptors in groups allowed for quick and easy access to analyses from a
    single, organised source.

    \subsection*{Matching algorithms}
    In order to match grains using the descriptor values, a matching algorithm
    was required. Initially a brute-force matcher was used to compare each
    descriptor value in the target to all values of the same descriptor type in
    the source. However, it quickly became apparent that this approach would be
    far too slow, particularly for a larger database.\\
    For this reason, a k-dimensional tree search algorithm was used in an
    effort to improve matching efficiency.  This approach produced the same
    results as the brute force matcher, but by arranging descriptors in a tree
    structure, a far more efficient search to find the best match was possible.
    This reduced matching time considerably.

    \subsection*{Synthesis and transformations} \label{sat}
    The final step in the program was to synthesise the matched output.
    This process consisted of:
    \begin{enumerate}
        \item Retrieving the best grain matches returned by the matching algorithm
        \item Applying a window function
        \item Overlapping the grains 
        \item Transforming grains to match the target
        \item Saving the result to a file
    \end{enumerate}
    Initially, grains were not transformed to better match the target.  This
    worked effectively for large databases; however, it was observed that
    results synthesised using small databases were of a lower quality, as the
    chance of a closely matched grain was lower. To account for this, methods
    for altering grains to better match their target were implemented.  It was
    decided that the two most significant characteristics to alter were the
    pitch and intensity of the grains.  By scaling the grains by the difference
    between the source and target RMS, it was possible to impose a closer
    intensity on a grain. Likewise, by shifting the pitch of a grain by the
    difference, it was possible to better match the pitch contour of the output
    to that of the target audio.  This improved the results significantly in
    smaller databases, as poor matches could be improved to match the target
    more convincingly.

    \subsection*{Command-line interface}
    In order to make the framework accessible to users, a command-line interface
    was developed. By supplying arguments to the program, users could alter
    parameters and experiment freely with the tool.  Although this interface
    was sufficient for testing and experimentation, it quickly became apparent
    that there were too many parameters to pass to the program via the command
    line interface on each run. A configuration file parser was created to
    address this issue, allowing users to specify default parameters that would
    be used by the program on each run. The combination of these interfaces
    provided an effective means for accessing all of the framework's features.

    \subsection*{Documentation and API}
    Complete documentation for the project was created in order to make the
    project as user friendly as possible for both developers and users.  As a
    result, a full API is available alongside examples of use and instructions
    for command-line operation. This was created in the hope that it might form
    a usable package that developers can build on quickly and effectively to
    build other CS projects, allowing for easier access to Python-based CS than
    is currently available. The command-line interface is equally documented to
    allow users to create their own realisations quickly and easily so that
    this project may be used for creative sound design purposes.

    \section*{Results and evaluation}
    Overall, the results generated by this project showed promise; a variety of
    transformations were generated using open source instrument databases to
    demonstrate the projects potential for sound design application. This
    tested the project's ability to convincingly impose qualities of an
    instrument onto target sounds. A variety of examples are provided that
    outline the style of synthesis aimed for. These range from imposing
    acoustic guitar qualities on an electric guitar to imposing stringed
    instrument qualities on vocal melodies. Current results have a clear
    synthetic nature, but still clearly exhibit some of the main
    characteristics of the database used.

    \section*{Research Limitations/Potential Development}
    In retrospect, a great deal of time was spent trying to improve the
    efficiency of the project. Although this was necessary, as initial tests
    were not feasible on most databases, it had a negative impact on the time
    available for developing perceptual qualities of the output. As a result of
    this, the overall quality of output might not perhaps be as natural as that
    of other projects in this area. This is apparent in the
    vocal~\textrightarrow~string instrument examples. Phrases tend to begin and
    end abruptly, failing to replicate any defined attack or decay of the
    string instruments, as would be expected when hearing a string instrument
    naturally. Conversely, this does give output it's own synthetic
    characteristic, which may be desirable as perfect reproduction of an
    instrument may not be the reason for using this tool.\\
    In addition, the amount of computation required results in large amounts of time
    needed to produce high quality results. An end user may not have the
    patience required to reach the quality of results that might be
    possible. This is in part a drawback of the Python language, and could be
    better accounted for with further work on profiling the performance of the
    tool.\\
    However, the fundamental concepts such as descriptor matching and
    transforming matches to better fit the target, which are used in the most
    sophisticated CS projects, have been implemented in this project to
    satisfying creative effect. As a proof of concept, this project displays
    the possibilities for CS in Python and there is evidently potential for
    further development in this area.\\

    There are a number of further improvements that could be made to this
    project in order to improve the quality of results and extend it's overall
    usefulness. These range from reasonably simple modifications that could not
    be implemented purely due to time constraints, to more complex ideas that
    may take a considerable amount of work. The following is a list of some
    initial ideas for improvements.\\

    \begin{itemize}
        \item The current implementation uses only a small and relatively basic
            subset of the audio descriptors available. This limits the analysis
            of audio and thus the quality of matches. Using a larger set of
            more advanced descriptors may improve quality from this
            perspective. One way would be to incorporate the open source
            Essentia audio descriptors~\parencite{Essentia2016} giving the
            project access to a vast quantity of descriptors for analysis.

        \item Replacing the hanning window function used for grain windowing
            with a short cross-fade at grain overlaps should reduce amplitude
            modulation, resulting in smoother transitions between grains. This
            might be further improved through calculating the point of maximum
            similarity by cross-correlating overlapping sections, as described
            by~\textcite[p.191-193]{Zolzer2011} in the Synchronus OverLap Add
            (SOLA) algorithm.

        \item A lack of continuity between grains was observed in results, most
            likely owing to the lack of any comparison of selected grains. A
            Viterbi algorithm could be used to account for this, allowing for a
            search to be done amongst the top matches to find the optimal set
            of grains. This takes advantage of the offline nature of the
            project and has been shown to work effectively in the Talkapillar
            project~\parencite{Hueber}.

        \item Although the HDF5 filesystem allows for easy storage of
            descriptor values, it also has drawbacks that limits the
            functionality of the project. One significant problem is that it is
            difficult to implement parallel processing using the library and
            for this reason asynchronous processing was not implemented in the
            project. An alternative method of storage may accommodate this more
            easily, allowing for the speed-ups possible through asynchronous
            processing. The overall design of the database management was also
            relatively naive and may benefit from being replaced by a
            technology such as an SQL database or similar. This has been shown
            to work effectively in work such as the CataRT
            project~\parencite[p.3]{Schwarz2006a}.
    \end{itemize}
   
    \section*{Conclusion}
    This project has provided a functioning Python based CS project with much
    potential for further development. Given the number of technical issues
    faced with this style of synthesis (from the big data issues faced with
    analysis storage, to high efficiency requirements for processing the large
    quantities of data), overall this project appears to work effectively. It
    provides a new and accessible means for tapping some of the vast amount of
    potential that concatenative synthesis has to offer.\\ 
    With the ever increasing quality of technology, it is predicted that new
    techniques such as concatenative synthesis may grow further in popularity,
    leading to an increasing number of possibilities in this area of sound
    synthesis. It is hoped that this project might aid in the highlighting the
    possibilities offered by this form of synthesis and demonstrate some of the
    technical obstacles that must be addressed to design a CS project
    successfully.

    \pagebreak

    \printbibliography
\end{document}
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