Nonlinear Spectral Subtraction Berbasis Tsallis Statistics Untuk Peningkatan Kualitas Sinyal Ucapan

Adanya derau (noise) mengurangi kualitas dan inteligibilitas dari sinyal ucapan dan ini berakibat menurunnya performa dari aplikasi berbasis sinyal ucapan. Pengurangan spektral (spectral subtraction) adalah salah satu metode yang populer untuk menghilangkan derau tersebut. Akan tetapi, pengurangan spektral memiliki kelemahan, yaitu memperkenalkan musical noise. Telah banyak turunan dari pengurangan spektral yang diusulkan untuk mengurangi musical noise. Salah satunya adalah menggunakan oversubtraction dalam formulasi pengurangan spektral. Pendekatan ini disebut nonlinear pengurangan spektral. Akan tetapi, penentuan faktor ini secara heuristik. Dengan menggunakan Tsallis statistics, nonlinear subtraksi dapat diturunkan secara matematis. Varian baru spectral subtraction yang disebut q-spectral subtraction telah diturunkan. Metode ini telah terbukti efektif untuk meningkatkan performa sistem pengenalan ucapan terhadap noise. Akan tetapi, evaluasi metode ini untuk meningkatkan kualitas sinyal ucapan pada speech enhancement belum diinvestigasi. Pada paper ini, performa q-SS untuk speech enhancement akan diivestigasi. Dari hasil percobaan, ditemukan bahwa q-SS lebih baik dalam meningkatkan kualitas sinyal ucapan dibandingkan metode pengurangan spektral lain

Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article accepted for publication in IET Signal Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusion