Sound archaeology: terminology, Palaeolithic cave art and the soundscape

This article is focused on the ways that terminology describing the study of music and sound within archaeology has changed over time, and how this reflects developing methodologies, exploring the expectations and issues raised by the use of differing kinds of language to define and describe such work. It begins with a discussion of music archaeology, addressing the problems of using the term ‘music’ in an archaeological context. It continues with an examination of archaeoacoustics and acoustics, and an emphasis on sound rather than music. This leads on to a study of sound archaeology and soundscapes, pointing out that it is important to consider the complete acoustic ecology of an archaeological site, in order to identify its affordances, those possibilities offered by invariant acoustic properties. Using a case study from northern Spain, the paper suggests that all of these methodological approaches have merit, and that a project benefits from their integration

Shift-Invariant Kernel Additive Modelling for Audio Source Separation

A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of-the-art approaches are supervised methods trained on large datasets, interest in non-data-driven approaches such as Kernel Additive Modelling (KAM) remains high due to their interpretability and adaptability. KAM performs the separation of a given source applying robust statistics on the time-frequency bins selected by a source-specific kernel function, commonly the K-NN function. This choice assumes that the source of interest repeats in both time and frequency. In practice, this assumption does not always hold. Therefore, we introduce a shift-invariant kernel function capable of identifying similar spectral content even under frequency shifts. This way, we can considerably increase the amount of suitable sound material available to the robust statistics. While this leads to an increase in separation performance, a basic formulation, however, is computationally expensive. Therefore, we additionally present acceleration techniques that lower the overall computational complexity.Comment: Feedback is welcom

Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning

Separating audio mixtures into individual instrument tracks has been a long standing challenging task. We introduce a novel weakly supervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover, a global regularization term is added to fulfill the spectrum energy preservation property regardless separation. Unlike state-of-the-art weakly supervised models which often involve deliberately devised constraints or careful model selection, our approach need little prior model specification on the data, and can be straightforwardly learned in an end-to-end fashion. We show that the proposed method performs competitively on public benchmark against state-of-the-art weakly supervised methods