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.

A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

Blind source separation is a widely used technique to analyze multichannel data. In most real-world applications, noise is inevitable and will affect the quality of signal separation and even make signal separation failure. In this paper, a new signal processing framework is proposed to separate noisy mixing sources. It is composed of two different stages. The first step is to process the mixing signal by a certain signal transform method to make the expected signals have energy concentration characteristics in the transform domain. The second stage is formed by a certain BSS algorithm estimating the demixing matrix in the transform domain. In the energy concentration segment, the SNR can reach a high level so that the demixing matrix can be estimated accurately. The analysis process of the proposed algorithm framework is analyzed by taking the wavelet transform as an example. Numerical experiments demonstrate the efficiency of the proposed algorithm to estimate the mixing matrix in comparison with well-known algorithms.

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

Factors influencing household-level positive and negative solid waste management practices in rapidly urbanizing cities: insights from Santa Cruz de la Sierra, Bolivia

Household solid waste management (HSWM) practices are a critical aspect of municipal solid waste management (MSWM) systems. Despite efforts to implement source separation and recycling at the household level in developing countries, negative practices such as illegal dumping and backyard burning remain ubiquitous, particularly in rapidly urbanizing cities. Source separation and recycling behaviors have been rarely studied in such cities. Moreover, studies on illegal dumping and backyard burning using robust tools and frameworks are practically non-existent. This study aims to (a) estimate the prevalence of “negative” and “positive” behaviors for different HSWM practices, and (b) identify their observable and non-observable influencing factors. The focus is Santa Cruz, a rapidly urbanizing city of Bolivia. Household surveys (n=305) are used to establish the connections between latent constructs (e.g. awareness, satisfaction), and observable variables (e.g. location, socio-demographic characteristics) with each behavior. This is achieved through the combination of exploratory factor analysis to validate the constructs to be included in the analysis, and structural equation modeling to identify the most influential factors. Two causal models are developed, one for the positive behaviors (i.e. source separation, recyclables donation, recyclables selling, and use of drop-off facilities), and the other for the negative behaviors (i.e. illegal dumping and backyard burning). Results indicate that, satisfaction with the MSWM service has a negative and significant influence on the prevalence of illegal dumping and backyard burning behaviors, while the remoteness of the household (i.e. distance to the city center) has a positive significant effect on the prevalence of these behaviors. Source separation and recyclable donation are influenced positively by latent constructs such as attitudes, knowledge, and awareness. For recyclables selling and use of drop-off stations, income and location are the most relevant factors, although with smaller effects.