A system for the automatic transcription of music is described. Signal processing methods are introduced that solve different facets of the overall problem. Main emphasis is laid on finding the multiple pitches of concurrent musical sounds. Sound onset detection and musical meter estimation are described to some extent. Other topics discussed are noise robustness, estimation of the number of concurrent voices, sound separation, and musical instrument recognition. The presented system is evaluated using a database of musical sounds, synthesized MIDI-songs, and CDrecordings. Also, the performance of the system is compared to that of human listeners. 1.

this article, we give an overview of a range of approaches to the analysis and separation of musical audio. In particular, we consider the problems of automatic music transcription and audio source separation, which are of particular interest to our group. Monophonic music transcription, where a single note is present at one time, can be tackled using an autocorrelationbased method. For polyphonic music transcription, with several notes at any time, other approaches can be used, such as a blackboard model or a multiple-cause/sparse coding method

We consider the problem of decomposing an observed input matrix (or vector sequence) into the product of a mixing ¡ matrix with a component ¢ matrix, ¡¥ ¢ i.e. where (a) the elements of the mixing matrix and the component matrix are non-negative, and (b) the underlying components are considered to be observations from an independent source. This is therefore a problem of non-negative independent component analysis. Under certain reasonable conditions, it appears to be sufficient simply to ensure that the output matrix has diagonal covariance (in addition to the non-negativity constraints) to find the independent basis. Neither higher-order statistics nor temporal correlations are required. The solution is implemented as a neural network with error-correcting forward/backward weights and linear anti-Hebbian lateral inhibition, and is demonstrated on small artificial data sets including a linear version of the Bars problem.

Over the last decade, an increasing number of researchers have become interested in the problem of Computational Auditory Scene Analysis (CASA), the use of computers to recognize sound sources in a complex auditory environment. In this paper, we give an overview of some approaches we are using in this area, and in particular for automatic music transcription and separation of audio sources.