5 accepted papers
Quality of sound source separation algorithms has traditionally been evaluated based on a set of established quality metrics: target distortion, interference from other sources, artifacts distortion, and a measure of overall separation quality. In our previous work, listening test results were prese…
In live and studio recordings unexpected sound events often lead to interferences in the signal. For non-stationary interferences, sound source separation techniques can be used to reduce the interference level in the recording. In this context, we present a novel approach combining the strengths of…
In this paper, we propose a simple user-assisted method for the recovery of repeating patterns in time and frequency which can occur in audio mixtures. Here, the user selects a region in a log-frequency spectrogram from which they seek to recover the underlying pattern, such as a repeating chord mas…
We propose a projection-based method for the unmixing of multichannel audio signals into their different constituent spatial objects. Here, spatial objects are modelled using a unified framework which handles both point sources and diffuse sources. We then propose a novel methodology to estimate and…
Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability ove…