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Existing audio tools handle the increasing amount of computer audio data inadequately. The typical tape-recorder paradigm for audio interfaces is inflexible and time consuming, especially for large data sets. On the other hand, completely automatic audio analysis and annotation is impossible using current techniques. Alternative solutions are semi-automatic user interfaces that let users interact with sound in flexible ways based on content. This approach offers significant advantages over manual browsing, annotation and retrieval. Furthermore, it can be implemented using existing techniques for audio content analysis in restricted domains. This paper describes MARSYAS, a framework for experimenting, evaluating and integrating such techniques. As a test for the architecture, some recently proposed techniques have been implemented and tested. In addition, a new method for temporal segmentation based on audio texture is described. This method is combined with audio analysis techniques and used for hierarchical browsing, classification and annotation of audio files.

We report on the tempo induction contest organized during the International Conference on Music Information Retrieval (ISMIR 2004) held at the University Pompeu Fabra in Barcelona, Spain, in October 2004. The goal of this contest was to evaluate some state-of-the-art algorithms in the task of inducing the basic tempo (as a scalar, in beats per minute) from musical audio signals. To our knowledge, this is the first published large scale cross-validation of audio tempo induction algorithms. Participants were invited to submit algorithms to the contest organizer, in one of several allowed formats. No training data was provided. A total of 12 entries (representing the work of seven research teams) were evaluated, 11 of which are reported in this document. Results on the test set of 3199 instances were returned to the participants before they were made public. Anssi Klapuri's algorithm won the contest. This evaluation shows that tempo induction algorithms can reach over 80% accuracy for music with a constant tempo, if we do not insist on finding a specific metrical level. After the competition, the algorithms and results were analyzed in order to discover general lessons for the future development of tempo induction systems. One conclusion is that robust tempo induction entails the processing of frame features rather than that of onset lists. Further, we propose a new "redundant" approach to tempo induction, inspired by knowledge of human perceptual mechanisms, which combines multiple simpler methods using a voting mechanism. Machine emulation of human tempo induction is still an open issue. Many avenues for future work in audio tempo tracking are highlighted, as for instance the definition of the best rhythmic features and the most appropriate periodicity detection method. In order to stimulate further research, the contest results, annotations, evaluation software and part of the data are available at http://ismir2004.ismir.net/ISMIR-Contest.html

Abstract. This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.

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In order to represent musical content, pitch and timing information is utilized in the majority of existing work in Symbolic Music Information Retrieval (MIR). Symbolic representations such as MIDI allow the easy calculation of such information and its manipulation. In contrast, most of the existing work in Audio MIR uses timbral and beat information, which can be calculated using automatic computer audition techniques. In this paper, Pitch Histograms are defined and proposed as a way to represent the pitch content of music signals both in symbolic and audio form. This representation is evaluated in the context of automatic musical genre classification. A multiple-pitch detection algorithm for polyphonic signals is used to calculate Pitch Histograms for audio signals. In order to evaluate the extent and significance of errors resulting from the automatic multiple-pitch detection, automatic musical genre classification results from symbolic and audio data are compared. The comparison indicates that Pitch Histograms provide valuable information for musical genre classification. The results obtained for both symbolic and audio cases indicate that although pitch errors degrade classification performance for the audio case, Pitch Histograms can be effectively used for classification in both cases.

The constantly increasing amount of audio available in digital form necessitates the development of software systems for analyzing and retrieving digital audio. In this work, we describe our efforts in developing such systems. More specifically, we describe the design philosophy behind our approach, the specific problems we try to solve, and how we evaluate the performance of our algorithms. Automatic music analysis and retrieval of non-speech digital audio is a relatively new field, and the existing techniques are far from perfect. To improve the performance of the developed techniques, two main techniques are used: (1) integration of information from multiple analysis and retrieval algorithms and (2) the use of graphical user interfaces that enable the user to provide feedback to the design, development, and evaluation of the algorithms. All the developed algorithms and user interfaces are integrated under MARSYAS, a software framework for research in computer audition.

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