ANALYSIS OF PORTABLE SYSTEM FOR SOUND ACQUISITION OF VEHICLES POWERED BY INTERNAL COMBUSTION ENGINES

In this paper a portable system for acquisition of sound generated by passenger vehicles powered by internal combustion engines is described and analyzed. The acquisition system is developed from scratch and tested in order to satisfy the requirements such as high-quality of audio recordings, high mobility, robustness and privacy respect. With this acquisition system and adequate signal processing, the main goal was to collect a large amount of clear audio recordings that will form a quality dataset. In further research, this dataset will be used for machine learning model training and testing, i.e. for developing a system for automatic recognition of the type of car engine based on fuel

CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds

The classification of environmental sounds (ESC) has been increasingly studied in recent years. The main reason is that environmental sounds are part of our daily life, and associating them with our environment that we live in is important in several aspects as ESC is used in areas such as managing smart cities, determining location from environmental sounds, surveillance systems, machine hearing, environment monitoring. The ESC is however more difficult than other sounds because there are too many parameters that generate background noise in the ESC, which makes the sound more difficult to model and classify. The main aim of this study is therefore to develop more robust convolution neural networks architecture (CNN). For this purpose, 150 different CNN-based models were designed by changing the number of layers and values of their tuning parameters used in the layers. In order to test the accuracy of the models, the Urbansound8k environmental sound database was used. The sounds in this data set were first converted into an image format of 32x32x3. The proposed CNN model has yielded an accuracy of as much as 82.5% being higher than its classical counterpart. As there was not that much fine-tuning, the obtained accuracy has been found to be better and satisfactory compared to other studies on the Urbansound8k when both accuracy and computational complexity are considered. The results also suggest further improvement possible due to low complexity of the proposed CNN architecture and its applicability in real-world settings

Audio segmentation-by-classification approach based on factor analysis in broadcast news domain

This paper studies a novel audio segmentation-by-classification approach based on factor analysis. The proposed technique compensates the within-class variability by using class-dependent factor loading matrices and obtains the scores by computing the log-likelihood ratio for the class model to a non-class model over fixed-length windows. Afterwards, these scores are smoothed to yield longer contiguous segments of the same class by means of different back-end systems. Unlike previous solutions, our proposal does not make use of specific acoustic features and does not need a hierarchical structure. The proposed method is applied to segment and classify audios coming from TV shows into five different acoustic classes: speech, music, speech with music, speech with noise, and others. The technique is compared to a hierarchical system with specific acoustic features achieving a significant error reduction

Multiclass audio segmentation based on recurrent neural networks for broadcast domain data

This paper presents a new approach based on recurrent neural networks (RNN) to the multiclass audio segmentation task whose goal is to classify an audio signal as speech, music, noise or a combination of these. The proposed system is based on the use of bidirectional long short-term Memory (BLSTM) networks to model temporal dependencies in the signal. The RNN is complemented by a resegmentation module, gaining long term stability by means of the tied state concept in hidden Markov models. We explore different neural architectures introducing temporal pooling layers to reduce the neural network output sampling rate. Our findings show that removing redundant temporal information is beneficial for the segmentation system showing a relative improvement close to 5%. Furthermore, this solution does not increase the number of parameters of the model and reduces the number of operations per second, allowing our system to achieve a real-time factor below 0.04 if running on CPU and below 0.03 if running on GPU. This new architecture combined with a data-agnostic data augmentation technique called mixup allows our system to achieve competitive results in both the Albayzín 2010 and 2012 evaluation datasets, presenting a relative improvement of 19.72% and 5.35% compared to the best results found in the literature for these databases