Sound Source Separation Mechanisms of Different Deep Networks Explained from the Perspective of Auditory Perception

Thanks to the development of deep learning, various sound source separation networks have been proposed and made significant progress. However, the study on the underlying separation mechanisms is still in its infancy. In this study, deep networks are explained from the perspective of auditory perception mechanisms. For separating two arbitrary sound sources from monaural recordings, three different networks with different parameters are trained and achieve excellent performances. The networks’ output can obtain an average scale-invariant signal-to-distortion ratio improvement (SI-SDRi) higher than 10 dB, comparable with the human performance to separate natural sources. More importantly, the most intuitive principle—proximity—is explored through simultaneous and sequential organization experiments. Results show that regardless of network structures and parameters, the proximity principle is learned spontaneously by all networks. If components are proximate in frequency or time, they are not easily separated by networks. Moreover, the frequency resolution at low frequencies is better than at high frequencies. These behavior characteristics of all three networks are highly consistent with those of the human auditory system, which implies that the learned proximity principle is not accidental, but the optimal strategy selected by networks and humans when facing the same task. The emergence of the auditory-like separation mechanisms provides the possibility to develop a universal system that can be adapted to all sources and scenes.

Active Binaural Auditory Perceptual System for a Socially Interactive Humanoid Robot

Sound Source Localization (SSL) and gaze shift to the sound source behavior is an integral part of a socially interactive humanoid robot perception system. In noisy and reverberant environments, it is non-trivial to estimate the location of a sound source and accurately shift gaze in its direction. Previous SSL algorithms are deficient in the optimum approximation of distance to audio sources and to accurately detect, interpret, and differentiate the actual sound from comparable sound sources due to challenging acoustic environments. In this article, a learning-based model is presented to achieve noiseless and reverberation-resistant sound source localization in the real-world scenarios. The proposed system utilizes a multi-layered Gaussian Cross-Correlation with Phase Transform (GCC-PHAT) signal processing technique as a baseline for a Generalized Cross Correlation Convolution Neural Network (GCC-CNN) model. The proposed model is integrated with an efficient rotation algorithm to predict and orient toward the sound source. The performance of the proposed method is compared with the state-of-art deep network-based sound source localization methods. The findings of the proposed method outperform the existing neural network-based approaches by achieving the highest accuracy of 96.21% for an active binaural auditory perceptual system.

Field Measurements for Traffic Noise Reduction in Highway Tunnels Using Closed-Cell Aluminum Foam Board

In order to explore the sound absorption and noise reduction performance of closed-cell aluminum foam in the tunnel, the field test of the sound absorption performance of aluminum foam board was carried out based on the installation of aluminum foam board in the whole line of Haoshanyu Tunnel on Qinglan Expressway. Combined with the existing loudspeaker test and typical tunnel measurements, a new field test method for the noise reduction performance of closed-cell aluminum foam board was proposed for two different working conditions including fixed-point pure tone sound source condition and mobile vehicle sound source condition. The testing results of the new methods were analyzed, and it showed that the closed-cell aluminum foam has good sound absorption property at the frequency spectra between 250 Hz and 1000 Hz, and the farther away from the sound source, the better the sound absorption effect. In the research on the noise reduction effect of actual vehicle, it was found that the insertion loss of the closed-cell foam aluminum board is about 4 dB(A), which indicated that the closed-cell aluminum foam can play a certain noise reduction effect in the tunnel.