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21	Voice Activity Detection and Noise Estimation for Teleconference Phones Eliasson, Björn January 2015 (has links) If communicating via a teleconference phone the desired transmitted signal (speech) needs to be crystal clear so that all participants experience a good communication ability. However, there are many environmental conditions that contaminates the signal with background noise, i.e sounds not of interest for communication purposes, which impedes the ability to communicate due to interfering sounds. Noise can be removed from the signal if it is known and so this work has evaluated different ways of estimating the characteristics of the background noise. Focus was put on using speech detection to define the noise, i.e. the non-speech part of the signal, but other methods not solely reliant on speech detection but rather on characteristics of the noisy speech signal were included. The implemented techniques were compared and evaluated to the current solution utilized by the teleconference phone in two ways, firstly for their speech detection ability and secondly for their ability to correctly estimate the noise characteristics. The evaluation process was based on simulations of the methods' performance in various noise conditions, ranging from harsh to mild environments. It was shown that the proposed method showed improvement over the existing solution, as implemented in this study, in terms of speech detection ability and for the noise estimate it showed improvement in certain conditions. It was also concluded that using the proposed method would enable two sources of noise estimation compared to the current single estimation source and it was suggested to investigate how utilizing two noise estimators could affect the performance. Voice Activity Detection (VAD) noise estimation continuous noise estimation (CNE) statistical model-based VAD likelihood ratio approach teleconferencing Mathematics Matematik
22	Towards a Nuanced Evaluation of Voice Activity Detection Systems : An Examination of Metrics, Sampling Rates and Noise with Deep Learning / Mot en nyanserad utvärdering av system för detektering av talaktivitet Joborn, Ludvig, Beming, Mattias January 2022 (has links) Recently, Deep Learning has revolutionized many fields, where one such area is Voice Activity Detection (VAD). This is of great interest to sectors of society concerned with detecting speech in sound signals. One such sector is the police, where criminal investigations regularly involve analysis of audio material. Convolutional Neural Networks (CNN) have recently become the state-of-the-art method of detecting speech in audio. But so far, understanding the impact of noise and sampling rates on such methods remains incomplete. Additionally, there are evaluation metrics from neighboring fields that remain unintegrated into VAD. We trained on four different sampling rates and found that changing the sampling rate could have dramatic effects on the results. As such, we recommend explicitly evaluating CNN-based VAD systems on pertinent sampling rates. Further, with increasing amounts of white Gaussian noise, we observed better performance by increasing the capacity of our Gated Recurrent Unit (GRU). Finally, we discuss how careful consideration is necessary when choosing a main evaluation metric, leading us to recommend Polyphonic Sound Detection Score (PSDS). voice activity detection VAD deep learning machine learning ML artificial intelligence AI convolutional neural network CNN deep neural network DNN sound event detection SED mel spectrogram audio processing polyphonic sound detection score PSDS signal processing signal to noise ratio SNR RCRNN sampling rate Gaussian noise Computer Sciences Datavetenskap (datalogi)

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Voice Activity Detection and Noise Estimation for Teleconference Phones

Towards a Nuanced Evaluation of Voice Activity Detection Systems : An Examination of Metrics, Sampling Rates and Noise with Deep Learning / Mot en nyanserad utvärdering av system för detektering av talaktivitet