Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. T. Theodorou, I. Mpoas, A. Lazaridis, N. Fakotakis, 'Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News', International Journal on Artificial Intelligence Tools, Vol. 26 (2), April 2017, 1750005 (13 pages), DOI: 10.1142/S021821301750005. © The Author(s).In this paper we describe an automatic sound recognition scheme for radio broadcast news based on principal component clustering with respect to the discrimination ability of the principal components. Specifically, streams of broadcast news transmissions, labeled based on the audio event, are decomposed using a large set of audio descriptors and project into the principal component space. A data-driven algorithm clusters the relevance of the components. The component subspaces are used by sound type classifier. This methodology showed that the k-nearest neighbor and the artificial intelligent network provide good results. Also, this methodology showed that discarding unnecessary dimension works in favor on the outcome, as it hardly deteriorates the effectiveness of the algorithms.Peer reviewe

Voice Activity Detection Based on Statistical Likelihood Ratio With Adaptive Thresholding

International audienceStatistical likelihood ratio test is a widely used voice activity detection (VAD) method, in which the likelihood ratio of the current temporal frame is compared with a threshold. A fixed threshold is always used, but this is not suitable for various types of noise. In this paper, an adaptive threshold is proposed as a function of the local statistics of the likelihood ratio. This threshold represents the upper bound of the likelihood ratio for the non-speech frames, whereas it remains generally lower than the likelihood ratio for the speech frames. As a result, a high non-speech hit rate can be achieved, while maintaining speech hit rate as large as possible

Model kompanzasyonlu birinci derece istatistikleri ile i-vektörlerin gürbüzlüğünün artırılması

Speaker recognition systems achieved significant improvements over the last decade, especially due to the performance of the i-vectors. Despite the achievements, mismatch between training and test data affects the recognition performance considerably. In this paper, a solution is offered to increase robustness against additive noises by inserting model compensation techniques within the i-vector extraction scheme. For stationary noises, the model compensation techniques produce highly robust systems. Parallel Model Compensation and Vector Taylor Series are considered as state-of-the-art model compensation techniques. Applying these methods to the first order statistics, a noisy total variability space training is aimed, which will reduce the mismatch resulted by additive noises. All other parts of the conventional i-vector scheme remain unchanged, such as total variability matrix training, reducing the i-vector dimensionality, scoring the i-vectors. The proposed method was tested with four different noise types with several signal to noise ratios (SNR) from -6 dB to 18 dB with 6 dB steps. High reductions in equal error rates were achieved with both methods, even at the lowest SNR levels. On average, the proposed approach produced more than 50% relative reduction in equal error rate.Konuşmacı tanıma sistemleri özellikle i-vektörlerin performansı sebebiyle son on yılda önemli gelişmeler elde etmiştir. Bu gelişmelere rağmen eğitim ve test verileri arasındaki uyumsuzluk tanıma performansını önemli ölçüde etkilemektedir. Bu çalışmada, model kompanzasyon yöntemleri i-vektör çıkarımı şemasına eklenerek toplanabilir gürültülere karşı gürbüzlüğü artıracak bir çözüm sunulmaktadır. Durağan gürültüler için model kompanzasyon teknikleri oldukça gürbüz sistemler üretir. Paralel Model Kompanzasyonu ve Vektör Taylor Serileri en gelişmiş model kompanzasyon tekniklerinden kabul edilmektedir. Bu metotlar birinci dereceden istatistiklere uygulanarak toplanabilir gürültülerden kaynaklanan uyumsuzluğu azaltacak gürültülü tüm değişkenlik uzayı eğitimi amaçlanmıştır. Tüm değişkenlik matrisin eğitimi, i-vektör boyutunun azaltılması, i-vektörlerin puanlanması gibi geleneksel i-vektör şemasının diğer tüm parçaları değişmeden kalmaktadır. Önerilen yöntem, 6 dB’lik adımlarla -6 dB’den 18 dB’ye kadar çeşitli sinyal-gürültü oranlarına (SNR) sahip dört farklı gürültü tipi ile test edilmiştir. Her iki yöntemle de en düşük SNR seviyelerinde bile eşit hata oranlarında yüksek azalmalar elde edilmiştir. Önerilen yaklaşım eşik hata oranında ortalama olarak %50’den fazla göreceli azalma sağlamıştır