Acoustic Scene Classification Using Multichannel Observation with Partially Missing Channels

Sounds recorded with smartphones or IoT devices often have partially unreliable observations caused by clipping, wind noise, and completely missing parts due to microphone failure and packet loss in data transmission over the network. In this paper, we investigate the impact of the partially missing channels on the performance of acoustic scene classification using multichannel audio recordings, especially for a distributed microphone array. Missing observations cause not only losses of time-frequency and spatial information on sound sources but also a mismatch between a trained model and evaluation data. We thus investigate how a missing channel affects the performance of acoustic scene classification in detail. We also propose simple data augmentation methods for scene classification using multichannel observations with partially missing channels and evaluate the scene classification performance using the data augmentation methods.

Utterance Clustering Using Stereo Audio Channels

Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was obtained to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multiperson discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono-audio signals in more complicated conditions.

Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks

We propose a method that uses ultrasound audio signals from a multichannel microphone array to estimate the positions of flying bats. The proposed model uses a deep convolutional neural network that takes multichannel signals as input and outputs the probability maps of the locations of bats. We present experimental results using two ultrasound audio clips of different bat species and show numerical simulations with synthetically generated sounds.

FEATURES OF ANALYSIS OF MULTICHANNEL AUDIO SIGNALSFEATURES OF ANALYSIS OF MULTICHANNEL AUDIO SIGNALS

The rapid growth of audio content has led to the need to use tools for analysis and quality control of audio signals using software and hardware and modules. The fastest-growing industry is software and programming languages.The Python programming language today has the most operational and visual capabilities for working with sound. When developing programs for computational signal analysis, it provides the optimal balance of high and low-level programming functions. Compared to Matlab or other similar solutions, Python is free and allows you to create standalone applications without the need for large, permanently installed files and a virtual environment.