... describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.

Abstract:- The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the temporal and spectral properties of non-stationary signals like audio. In this paper we describe some applications of the DWT to the problem of extracting information from non-speech audio. More specifically automatic classification of various types of audio using the DWT is described and compared with other traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes of music is presented. Both synthetic and real world stimuli were used to evaluate the performance of the beat detection algorithm. Key-Words:- audio analysis, wavelets, classification, beat extraction 1

The majority of existing work in music information retrieval for audio signals has followed the content-based query-by-example paradigm. In this paradigm a musical piece is used as a query and the result is a list of other musical pieces ranked by their content similarity. In this paper we describe algorithms and graphical user interfaces that enable novel alternative ways for querying and browsing large audio collections. Computer audition algorithms are used to extract content information from audio signals. This automatically extracted information is used to configure the graphical user interfaces and to genereate new query audio signals for browsing and retrieval. 1

We present the MIRtoolbox, an integrated set of functions written in Matlab, dedicated to the extraction of musical features from audio files. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches – including new strategies we have developed –, that users can select and parametrize. This paper offers an overview of the set of features, related, among others, to timbre, tonality, rhythm or form, that can be extracted with the MIRtoolbox. One particular analysis is provided as an example. The toolbox also includes functions for statistical analysis, segmentation and clustering. Particular attention has been paid to the design of a syntax that offers both simplicity of use and transparent adaptiveness to a multiplicity of possible input types. Each feature extraction method can accept as argument an audio file, or any preliminary result from intermediary stages of the chain of operations. Also the same syntax can be used for analyses of single audio files, batches of files, series of audio segments, multi-channel signals, etc. For that purpose, the data and methods of the toolbox are organised in an object-oriented architecture. 1

Abstract—This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user’s favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added. Index Terms—Aspect model, hybrid collaborative and contentbased recommendation, incremental training, music recommender system, probabilistic generative model. I.

Abstract. Music is not only a set of sounds, it evokes emotions, subjectively perceived by listeners. The growing amount of audio data available on CDs and in the Internet wakes up a need for content-based searching through these files. The user may be interested in finding pieces in a specific mood. The goal of this paper is to elaborate tools for such a search. A method for the appropriate objective description (parameterization) of audio files is proposed, and experiments on a set of music pieces are described. The results are summarized in concluding chapter. 1

Figure 0. A ChucK-based programming model for building audio analysis and synthesis programs. In this paper, we present a new programming model for performing audio analysis, spectral processing, and feature extraction in the ChucK programming language. The solution unifies analysis and synthesis in the same high-level, strongly-timed, and concurrent environment, extending and fully integrating with the existing language framework. In particular, we introduce the notion of a Unit Analyzer (UAna) and new constructs for dataflow, data types and semantics for operations in analysis domains, and mechanisms for seamlessly combining analysis and synthesis tasks in a precise, sample-synchronous manner. We present the motivation of our system, and describe new language-level syntaxes, semantics, and the underlying implementation. We provide code examples and discuss potential uses and benefits of the system for audio researchers, performers, and teachers. 1.

We present sndtools, a set of cross platform, open-source tools for simultaneously displaying related audio and visual information in real-time. The distribution includes tools to extract spectral information, perform linear predictive coding analysis and resynthesis, manipulate pitch and time using a phase vocoder, and map text to Morse code. Each tool has closely related audio and visual (graphical or text) components and can be used for instructive purposes or experimentation with sound. We show that hardware-accelerated graphics tools such as OpenGL can be used to enable real-time 3D visualization of DSP algorithms. 1.

This paper describes the FeatSynth framework, a set of open-source C++ classes intended to make it as easy as possible to integrate feature-based synthesis techniques into audio software. We briefly review the key ideas behind feature-based synthesis, and then discuss the framework’s architecture. We emphasize design choices meant to make the framework more flexible and straightforward to use. A number of illustrative examples of applications and tools developed using FeatSynth are presented to highlight different ways in which the API can be used. These examples include a command-line system for performing (among other things) offline non-phonorealistic analysis-synthesis transformations, a system allowing users to interactively manipulate the feature values used to control synthesis, and a ChucK FeatSynth plugin making use of the new “Chugin ” ChucK plugin framework. The framework can be downloaded from

This paper describes experiments on sonifying data obtained using the VICON motion capture system. The main goal is to build the necessary infrastructure in order to be able to map motion parameters of the human body to sound. For sonification the following three software frameworks were used: Marsyas, traditionally used for music information retrieval with audio analysis and synthesis, CHUCK, an on-the-fly real-time synthesis language, and Synthesis Toolkit (STK), a toolkit for sound synthesis that includes many physical models of instruments and sounds. An interesting possibility is the use of motion capture data to control parameters of digital audio effects. In order to experiment with the system, different types of motion data were collected. These include traditional performance on musical instruments, acting out emotions as well as data from individuals having impairments in sensor motor coordination. Rhythmic motion (i.e. walking) although complex, can be highly periodic and maps quite naturally to sound. We hope that this work will eventually assist patients in identifying and correcting problems related to motor coordination through sound.