Evaluation of the Processing Times in Anuran Sound Classification

Nowadays, sound classification applications are becoming more common in the Wireless Acoustic Sensor Networks (WASN) scope. However, these architectures require special considerations, like looking for abalance between transmitted data and local processing.This article proposes an audio processing and classification scheme, focusing on WASN architectures.This article also analyzes in detail the time efficiency of the different stages involved (from acquisition to classification). This study provides useful information which makes it possible to choose the best tradeoff between processing time and classification result accuracy. This approach has been evaluated on a wide set of anurans songs registered in their own habitat. Among the conclusions of this work, there is an emphasis on the disparity in the classification and feature extraction and construction times for the different studied techniques,all of them notably depending on the over all feature number used.Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía, Spain, through the Excellence Project eSAPIENS (Ref. TIC-5705

Identificação automática de aves a partir de áudio

Bird classification from audio is mainly useful for ornithologists and ecologists. With growing amounts of data, manual bird classification is time-consuming, which makes it a costly method. Birds react quickly to environmental changes, which makes their analysis an important problem in ecology, as analyzing bird behaviour and population trends helps detect other organisms in the environment. A reliable methodology that automatically identifies bird species from audio would be a valuable tool for the experts in the area. The main purpose of this work is to propose a methodology able to identify a bird species by its chirp. There are many techniques that can be used to process the audio data, and to classify the audio data. This thesis explores the deep learning techniques that are being used in this domain, such as using Convolutional Neural Networks and Recurrent Neural Networks to classify the data. Audio problems in deep learning are commonly approached by converting them into images using feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. Multiple deep learning and feature extraction combinations are used and compared in this thesis in order to find the most suitable approach to this problem.Classificação de pássaros a partir de áudio é principalmente útil para ornitólogos e ecologistas. Com o aumento da quantidade de dados disponível, classificar a espécie dos pássaros manualmente acaba por consumir muito tempo. Os pássaros reagem rapidamente às alterações climáticas, o que faz com que a análise de pássaros seja um problema interessante na ecologia, porque ao analisar o comportamento das aves e a tendência populacional, outros organismos podem ser detetados no meio ambiente. Devido a estes factos, a criação de uma metodologia que identifique a espécie dos pássaros fiavelmente seria uma ferramenta bastante útil para os especialistas na área. O objetivo principal do trabalho nesta dissertação é propor uma metodologia que identifique a espécie de uma ave através do seu canto. Existem diversas técnicas que podem ser usadas para processar os dados sonoros que contêm os cantos das aves, e que podem ser usadas para classificar as espécies das aves. Esta dissertação explora as principais técnicas de deep learning que são usadas neste domínio, tais como as redes neuronais convolucionais e as redes neuronais recorrentes que são usadas para classificar os dados. Os problemas relacionados com som no deep learning, são normalmente abordados por converter os dados sonoros em imagens utilizando técnicas de extração de atributos, para depois serem classificados utilizando modelos de deep learning tipicamente utilizados para classificar imagens. Dois exemplos destas técnicas de extração de atributos normalmente utilizadas são os Espectrogramas de Mel e os Coeficientes Cepstrais da Frequência de Mel. Nesta dissertação, são feitas múltiplas combinações de técnicas de deep learning com técnicas de extração de atributos do som. Estas combinações são utilizadas para serem comparadas com o âmbito de encontrar a abordagem mais apropriada para o problema

Effects of Noise Bandwidth and Amplitude Modulation on Masking in Frog Auditory Midbrain Neurons

Natural auditory scenes such as frog choruses consist of multiple sound sources (i.e., individual vocalizing males) producing sounds that overlap extensively in time and spectrum, often in the presence of other biotic and abiotic background noise. Detection of a signal in such environments is challenging, but it is facilitated when the noise shares common amplitude modulations across a wide frequency range, due to a phenomenon called comodulation masking release (CMR). Here, we examined how properties of the background noise, such as its bandwidth and amplitude modulation, influence the detection threshold of a target sound (pulsed amplitude modulated tones) by single neurons in the frog auditory midbrain. We found that for both modulated and unmodulated masking noise, masking was generally stronger with increasing bandwidth, but it was weakened for the widest bandwidths. Masking was less for modulated noise than for unmodulated noise for all bandwidths. However, responses were heterogeneous, and only for a subpopulation of neurons the detection of the probe was facilitated when the bandwidth of the modulated masker was increased beyond a certain bandwidth – such neurons might contribute to CMR. We observed evidence that suggests that the dips in the noise amplitude are exploited by TS neurons, and observed strong responses to target signals occurring during such dips. However, the interactions between the probe and masker responses were nonlinear, and other mechanisms, e.g., selective suppression of the response to the noise, may also be involved in the masking release