Question: If the received system structure is changed, does the synperiodic filter bank still generalize well?
Evidence: The fundamental task for an agent to perceive and interact with the 3D environment is to know the location and semantic identity of its nearby objects. The location includes spatial location that is either stationary or moving, temporal location like start time and end time. Vision-based such environment perception has received large attention in the past decade and we have witnessed huge progress in tasks such as object detection, classification and tracking. Nevertheless, the sound-based counterpart research has far lagged behind, despite all the fascinating properties sound signal exhibits. For example, sound is ubiquitous and insensitive to ambient illumination change, it has no field-of-view (FoV) constraints and is capable of circumventing physical barriers to perceive scene beyond line-of-sight. As a complementary sensing approach to vision, sound-based environment perception is of vital importance for acoustic scene understanding. A typical example is the sound source detection (answer where is it, when does it happen and what is it), given the recorded multi-channel sound waveforms.
To detect sound sources, we often deploy a spatially-configured microphone array to record an acoustic environment. Unlike camera or LiDAR scanner that directly captures RGB image or measures the range distance, sound waveform itself is a highly compressed one-dimensional points with high sampling rate, all sound source signals are compressed and mixed into the one-dimensional data format. Since different sound sources have different frequency property, it is essential convert waveform into time-frequency representation so that frequencies hidden in the waveform are explicitly split out. This is often achieved by projecting the raw waveform into various orthogonal frequency basis. At the same time, a sound source's spatial location clue lies in inter-channel difference of recorded waveforms (i.e. phase difference in this work). It is essential to design a neural network that jointly encodes mono-channel time-frequency representation and inter-channel phase difference from the raw waveforms in a unified, parameter-frugal and computation-efficient manner. The learned representation should have elegant resolution in both time, frequency and space domain so that sound sources can be precisely detected.
However, learning such representation is a tough task. Challenges derive from both theoretical side and practical side. According to Uncertainty Principle, we cannot achieve the optimal resolution in time and frequency domain at the same time, but instead keep a trade-off between them. Traditional hand-engineered sound feature and some recently proposed learnable filter bank empirically set the same length for all filters, resulting in human-biased, unadjustable time-frequency resolution map. Some other work correlates filter frequency response and filter length by initializing in mel-scale, but it is not scalable nor stable because the final initialization depends on the filter number. Moreover, all existing methods process raw waveform with one-scale filter bank, we argue that the one-scale sound perception easily leads to incomprehensive sound sources sensing, especially when sound sources have different frequency property or undergo various spatial motion.
In this paper, we first give comprehensive theoretical analysis on the filter bank impact on its extracted feature's time and frequency resolution. Based on the analysis, we propose a simple yet effective synperiodic filter bank construction strategy in which each filter's frequency response and length are correlated by rotating periodicity such that each filter's length is inversely proportional to its frequency resolution. The synperiodic filter bank thus internally maintains a better time-frequency resolution trade-off than traditional fixed-length filter bank. Coupling the filter length with its frequency response helps us to reduce human intervention in filter bank design. By simply alternating the periodicity term, we further construct a group of synperiodic filter banks, with which we achieve multi-scale perception in time domain. At the same time, by applying a synperiodic filter bank to process one raw waveform as well as its consecutively-downsampled versions, we achieve multi-scale perception in frequency domain. The multi-scale perception in both time and frequency domain of synperiodic filter bank enables the neural network to dynamically learn better representation for sound source detection in a data-driven way. It is worth noting that synperiodic filter bank parameter number is just linear to filter number (adds up to less than 1% of the whole parameters) and it can be efficiently implemented as a 1D convolution operator.
Following the aforementioned learnable front-end, we add a Transformer-like backbone network with two paralleling branches with intermediate soft-parameter sharing to learn sound source's semantic and spatial location related representation both jointly and separately. Experiment on both direction-of-arrival (DoA) task and physical location estimation task shows that our proposed framework outperforms comparing methods by a large margin. Replacing existing method's head with our proposed synperiodic filter bank also improvdes the performance.
The fundamental task for an agent to perceive and interact with the 3D environment is to know the location and semantic identity of its nearby objects. The location includes spatial location that is either stationary or moving, temporal location like start time and end time. Vision-based such environment perception has received large attention in the past decade and we have witnessed huge progress in tasks such as object detection, classification and tracking. Nevertheless, the sound-based counterpart research has far lagged behind, despite all the fascinating properties sound signal exhibits. For example, sound is ubiquitous and insensitive to ambient illumination change, it has no field-of-view (FoV) constraints and is capable of circumventing physical barriers to perceive scene beyond line-of-sight. As a complementary sensing approach to vision, sound-based environment perception is of vital importance for acoustic scene understanding. A typical example is the sound source detection (answer where is it, when does it happen and what is it), given the recorded multi-channel sound waveforms.
To detect sound sources, we often deploy a spatially-configured microphone array to record an acoustic environment. Unlike camera or LiDAR scanner that directly captures RGB image or measures the range distance, sound waveform itself is a highly compressed one-dimensional points with high sampling rate, all sound source signals are compressed and mixed into the one-dimensional data format. Since different sound sources have different frequency property, it is essential convert waveform into time-frequency representation so that frequencies hidden in the waveform are explicitly split out. This is often achieved by projecting the raw waveform into various orthogonal frequency basis. At the same time, a sound source's spatial location clue lies in inter-channel difference of recorded waveforms (i.e. phase difference in this work). It is essential to design a neural network that jointly encodes mono-channel time-frequency representation and inter-channel phase difference from the raw waveforms in a unified, parameter-frugal and computation-efficient manner. The learned representation should have elegant resolution in both time, frequency and space domain so that sound sources can be precisely detected.
However, learning such representation is a tough task. Challenges derive from both theoretical side and practical side. According to Uncertainty Principle, we cannot achieve the optimal resolution in time and frequency domain at the same time, but instead keep a trade-off between them. Traditional hand-engineered sound feature and some recently proposed learnable filter bank empirically set the same length for all filters, resulting in human-biased, unadjustable time-frequency resolution map. Some other work correlates filter frequency response and filter length by initializing in mel-scale, but it is not scalable nor stable because the final initialization depends on the filter number. Moreover, all existing methods process raw waveform with one-scale filter bank, we argue that the one-scale sound perception easily leads to incomprehensive sound sources sensing, especially when sound sources have different frequency property or undergo various spatial motion.
In this paper, we first give comprehensive theoretical analysis on the filter bank impact on its extracted feature's time and frequency resolution. Based on the analysis, we propose a simple yet effective synperiodic filter bank construction strategy in which each filter's frequency response and length are correlated by rotating periodicity such that each filter's length is inversely proportional to its frequency resolution. The synperiodic filter bank thus internally maintains a better time-frequency resolution trade-off than traditional fixed-length filter bank. Coupling the filter length with its frequency response helps us to reduce human intervention in filter bank design. By simply alternating the periodicity term, we further construct a group of synperiodic filter banks, with which we achieve multi-scale perception in time domain. At the same time, by applying a synperiodic filter bank to process one raw waveform as well as its consecutively-downsampled versions, we achieve multi-scale perception in frequency domain. The multi-scale perception in both time and frequency domain of synperiodic filter bank enables the neural network to dynamically learn better representation for sound source detection in a data-driven way. It is worth noting that synperiodic filter bank parameter number is just linear to filter number (adds up to less than 1% of the whole parameters) and it can be efficiently implemented as a 1D convolution operator.
Following the aforementioned learnable front-end, we add a Transformer-like backbone network with two paralleling branches with intermediate soft-parameter sharing to learn sound source's semantic and spatial location related representation both jointly and separately. Experiment on both direction-of-arrival (DoA) task and physical location estimation task shows that our proposed framework outperforms comparing methods by a large margin. Replacing existing method's head with our proposed synperiodic filter bank also improvdes the performance.
To detect sound sources, we often deploy a spatially-configured microphone array to record an acoustic environment. Unlike camera or LiDAR scanner that directly captures RGB image or measures the range distance, sound waveform itself is a highly compressed one-dimensional points with high sampling rate, all sound source signals are compressed and mixed into the one-dimensional data format. Since different sound sources have different frequency property, it is essential convert waveform into time-frequency representation so that frequencies hidden in the waveform are explicitly split out. This is often achieved by projecting the raw waveform into various orthogonal frequency basis. At the same time, a sound source’s spatial location clue lies in inter-channel difference of recorded waveforms (i.e. phase difference in this work). It is essential to design a neural network that jointly encodes mono-channel time-frequency representation and inter-channel phase difference from the raw waveforms in a unified, parameter-frugal and computation-efficient manner. The learned representation should have elegant resolution in both time, frequency and space domain so that sound sources can be precisely detected.****Following the aforementioned learnable front-end, we add a Transformer-like backbone network with two paralleling branches with intermediate soft-parameter sharing to learn sound source’s semantic and spatial location related representation both jointly and separately. Experiment on both direction-of-arrival (DoA) task and physical location estimation task shows that our proposed framework outperforms comparing methods by a large margin. Replacing existing method’s head with our proposed synperiodic filter bank also improvdes the performance.
Answer: Synperiodic filter bank is proposed to extract mono-channel time-frequency representation in multi-scale manner in both time and frequency domain. Theoretically, it can be used as a general sound waveform time-frequency representation extractor to solve other acoustic tasks (although we did not test it in this paper). If the system structure is changed, we think synperiodic filter bank can still be used as sound feature extraction front-end. In this paper, we have tested synperiodic filter bank generalizes well on both direction-of-arrival (DoA) task and sound source physical location ([x,y,z] coordinate) estimation task. It needs more test if the receiver configuration changes too much (i.e. rather than setting four microphones close with each other, but putting them far apart and at different corners)