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We propose a new sound onset detection method using principles of psychoacoustics. Our algorithm mimics the human auditory system to detect onsets in polyphonic audio.A filterbank based processing scheme is used that allows for a more robust detection of onsets using a popular model of loudness. We show the versatility of our algorithm by evaluating it's performance over a wide range of sounds.

In music compositions, certain parts may be played in an improvisational style with a rather vague notion of tempo, while other parts are characterized by having a clearly perceivable tempo. Based on this observation, we introduce in this paper some novel audio features for capturing tempo-related information. Rather than measuring the specific tempo of a local section of a given recording, our objective is to capture the existence or absence of a notion of tempo, a kind of tempo salience. By a quantitative analysis within an Indian music scenario, we demonstrate that our audio features capture the aspect of tempo salience well, while being independent of continuous fluctuations and local changes in tempo

This paper describes an audio retrieval system,Quebex,that works on raw audio data. The system is able to retrieve songs that are rhythmically and timbrewise similar from a database of 700 songs. A new algorithm is proposed that attempts to match two songs temporally in terms of timbre,using a simple dynamic time warping algorithm. A sieving technique is used to eliminate unlikely candidates from further processing and a ranking system among the rhythm, spectral and temporal features is used to retrieve songs. Initial results have shown great promise for the approach as it retrieves remixes and dubbed songs within the top 10 retrievals.