The rhythmic dimension in fiddle-playing as the music moves to newer performing and learning contexts

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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

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 Factoristiaon 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, to separate sound sources which have time evolving spectra from multi-channel signals

Key Signature Estimation

The problem of automatic key signature detection has been the focus of much research in recent years. Previous methods of key estimation have focused on chromagrams and key profiling techniques. This paper presents a remarkably simple but effective method of estimating key signature from musical recordings. The algorithm introduces the keyogram , a concept resembling the chromagram, and is aimed for use on traditional Irish music. The keyogram is a measure of the likelihood of each possible major key signature based on a masked scoring system

Resynthesis Methods for Sound Source Separation using Shifted Non-negative Factorisation Models

Recently, techniques such as shifted 2D non-negative matrix factorisation and shifted 2D non-negative tensor factorisations have been proposed as methods for separating harmonic musical instruments from single and multi-channel mixtures. However, these methods require the use of a Constant Q transform, for which no true inverse exists. This has adverse effects on the quality of the resynthesis of the separated sources. In this paper, a number of different resynthesis methods are investigated in order to determine the best approach to resynthesi

On the use of the Beta Divergence for Musical Source Separation

Non-negative Tensor Factorisation based methods have found use in the context of musical sound source separation. These techniques require the use of a suitable cost function to determine the optimal factorisation, and most work has focused on the use of the generalised Kullback-Liebler divergence, and more recently the Itakura-Saito divergence. These divergences can be regarded as limiting cases of the parameterised Beta divergence. This paper looks at the use of the Beta Divergence in the context of musical source separation with a view to determining an optimal value of Beta for this problem. This is considered for both magnitude and power spectrograms. In an effort to avoid potential local minima in the Beta divergence, the use of a “tempered” Beta Divergence is also explored

Musical Source Separation using Generalised Non-negative Tensor Factorisation Models

A shift-invariant non-negative tensor factorisation algorithm for musical source separation is proposed which generalises previous work by allowing each source to have its own parameters rather a fixed set of parameters for all sources. This allows independent control of the number of allowable notes, number of harmonics and shifts in time for each source. This increased flexibility allows the incorporation of further information about the sources and results in improved separation and resynthesis of the separated sources

Using Tensor Factorisation Models to Separate Drums from Polyphonic Music

This paper describes the use of Non-negative Tensor Factorisation models for the separation of drums from polyphonic audio. Improved separation of the drums is achieved through the incorporation of Gamma Chain priors into the Non-negative Tensor Factorisation framework. In contrast to many previous approaches, the method used in this paper requires little or no pre-training or use of drum templates. The utility of the technique is shown on real-world audio examples