Beat Tracking by Dynamic Programming

Beat tracking — i.e. deriving from a music audio signal a sequence of beat instants that might correspond to when a human listener would tap his foot — involves satisfying two constraints. On the one hand, the selected instants should generally correspond to moments in the audio where a beat is indicated, for instance by the onset of a note played by one of the instruments. On the other hand, the set of beats should reflect a locally-constant inter-beat-interval, since it is this regular spacing between beat times that defines musical rhythm. These dual constraints map neatly onto the two constraints optimized in dynamic programming, the local match, and the transition cost. We describe a beat tracking system which first estimates a global tempo, uses this tempo to construct a transition cost function, then uses dynamic programming to find the best-scoring set of beat times that reflect the tempo as well as corresponding to moments of high 'onset strength' in a function derived from the audio. This very simple and computationally efficient procedure is shown to perform well on the MIREX-06 beat tracking training data, achieving an average beat accuracy of just under 60% on the development data. We also examine the impact of the assumption of a fixed target tempo, and show that the system is typically able to track tempo changes in a range of ±10% of the target tempo

On Communicating Computational Research

Prof. Ellis's presentation focuses on the challenges, and the benefits, of sharing the results of computational research through various methods, including: traditional publications, public talks, interactive online demos, APIs and libraries, and code sharing. He particularly emphasizes the potential of code sharing in a world where commodity machines can make reproducibility increasingly affordable and attainable

Semantic Audio Analysis

An overview of the current status and applications of semantic audio analysis

Model-Based Separation in Humans and Machines

Comparing human performance on source separation with different automatic approaches, and arguing for (a) using models, and (b) concentrating on the content, not the signal per se

Classifying Music Audio with Timbral and Chroma Features

Music audio classification has most often been addressed by modeling the statistics of broad spectral features, which, by design, exclude pitch information and reflect mainly instrumentation. We investigate using instead beat-synchronous chroma features, designed to reflect melodic and harmonic content and be invariant to instrumentation. Chroma features are less informative for classes such as artist, but contain information that is almost entirely independent of the spectral features, and hence the two can be profitably combined: Using a simple Gaussian classifier on a 20-way pop music artist identification task, we achieve 54% accuracy with MFCCs, 30% with chroma vectors, and 57% by combining the two. All the data and Matlab code to obtain these results are available

What Can We Learn from Large Music Databases?

An overview of several of the music-related projects at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University

Searching and Describing Audio Databases

A summary of topics, including music information extraction and similarity matching, meeting recordings, and personal audio "lifelog" archives

Auditory Scene Analysis in Humans and Machines

Tutorial on auditory scene analysis and source separation in humans and machines

Speech Separation in Humans and Machines

An overview of the problem of separating speech in acoustic mixtures, including some perceptual results, brief introductions to ICA and CASA, and a pitch for model-based analysis