Martin Raspaud

Explicit modeling of temporal dynamics within musical signals for acoustical unit similarity

Timbre is a major cue for the human auditory system to recognize musical sounds. To describe timbre, the temporal dynamics is an important component as well as the widely used spectral envelope. In this paper, we present new temporal dynamics similarity measures, which will prove valuable for the recognition of timbral patterns. These similarity measures are evaluated, first alone, then in conjunction with spectral envelope similarity measures, for both single tones and solo recordings. Results are provided, showing that the new temporal dynamics features significantly improve timbral pattern recognition.

Spectral similarity metrics for sound source formation based on the common variation cue

Scene analysis is a relevant way of gathering information about the structure of an audio stream. For content extraction purposes, it also provides prior knowledge that can be taken into account in order to provide more robust results for standard classification approaches. In order to perform such scene analysis, we believe that the notion of temporality is important. Consequently, we study in this paper a new way of modeling the evolution over time of the frequency and amplitude parameters of spectral components. We evaluate its benefits by considering its ability to automatically gather the components of the same sound source. The evaluation of the proposed metric shows that it achieves good performance and takes better account of micro-modulations.

Spectral Similarity Metrics for Sound Source Formation Based on the Common Variation Cue

Scene analysis is a relevant way of gathering informations about the structure of an audio stream. For content extraction purposes, it also provides prior knowledge that can be taken into account in order to provide more robust results for standard classification approaches. In order to perform such scene analysis, we believe that the notion of temporality is important. We study in this paper a new way of modeling the evolution over time of the frequency and amplitude parameters of spectral components. We evaluate the benefits of such an approach by considering its ability to automatically gather the components of the same sound source. The evaluation of the proposed metric shows that it achieves good performance and take better account of micro-modulations.