Matt Cranitch

Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation

Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model. Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously.

Shifted non-negative matrix factorisation for sound source separation

A shifted non-negative matrix factorisation algorithm is derived, which offers advantages over previous matrix factorisation techniques for the purposes of single channel source separation. It represents a sound source as translations of a single frequency basis function. These translations approximately correspond to notes played by an instrument. Results are presented for a set of synthetic data, and on a single channel recording of piano and clarinet. Though the system is aimed at musical recordings, the technique can be applied to any data which contains shifted versions of an underlying factor, and so the algorithm could possibly be used in other applications such as image processing

Sound Source Separation Using Shifted Non-Negative Tensor Factorisation

Recently, shifted non-negative matrix factorisation was developed as a means of separating harmonic instruments from single channel mixtures. However, in many cases two or more channels are available, in which case it would be advantageous to have a multichannel version of the algorithm. To this end, a shifted non-negative tensor factorisation algorithm is derived, which extends shifted non-negative matrix factorisation to the multi-channel case. The use of this algorithm for multi-channel sound source separation of harmonic instruments is demonstrated. Further, it is shown that the algorithm can be used to perform non-negative tensor deconvolution, a multi-channel version of non-negative matrix deconvolution, to separate sound sources which have time evolving spectra from multi-channel signals

Interactive Music Archive Access System

The goal of the Interactive Music Archive Access System (IMAAS) project was to develop an interactive music archive access system which was capable of allowing an end-user to easily extract rhythmic, melodic and harmonic musical metadata descriptors from audio, and allow the user to interact with the archive contents in a manner not typically allowed in archive access systems. To this end, the IMAAS system incorporates a range of real-time interaction tools which allow the user to modify the retrieved audio in a number of ways including the ability to isolate individual instruments in stereo mixes, pitch and time-scale modification, and beat-synchronous looping. This demo gives an overview of the capabilities of the IMAAS application.