Towards a Generalization of Relative Transfer Functions to More Than One Source
This work addresses a fundamental limitation in audio signal processing for scenarios with multiple sources, such as in robotics or hearing aids, by providing a novel theoretical and practical extension, though it appears incremental in advancing RTF concepts.
The paper tackled the problem of generalizing relative transfer functions (RTFs) to handle multiple sound sources, proving it's impossible with single observations and introducing a new transform for multichannel multi-frame spectrograms that satisfies key RTF properties. Through simulated experiments, they demonstrated the method can localize multiple simultaneous sources using short windows without source separation, achieving unspecified localization results.
We propose a natural way to generalize relative transfer functions (RTFs) to more than one source. We first prove that such a generalization is not possible using a single multichannel spectro-temporal observation, regardless of the number of microphones. We then introduce a new transform for multichannel multi-frame spectrograms, i.e., containing several channels and time frames in each time-frequency bin. This transform allows a natural generalization which satisfies the three key properties of RTFs, namely, they can be directly estimated from observed signals, they capture spatial properties of the sources and they do not depend on emitted signals. Through simulated experiments, we show how this new method can localize multiple simultaneously active sound sources using short spectro-temporal windows, without relying on source separation.