By Jacob Benesty, Jingdong Chen, Yiteng Huang, Israel Cohen
Noise is all over the place and in so much purposes which are concerning audio and speech, corresponding to human-machine interfaces, hands-free communications, voice over IP (VoIP), listening to aids, teleconferencing/telepresence/telecollaboration structures, and such a lot of others, the sign of curiosity (usually speech) that's picked up via a microphone is usually infected through noise. hence, the microphone sign needs to be wiped clean up with electronic sign processing instruments prior to it's kept, analyzed, transmitted, or performed out. This cleansing method is frequently known as noise relief and this subject has attracted a large amount of examine and engineering awareness for numerous many years. one of many ambitions of this e-book is to provide in a typical framework an outline of the cutting-edge of noise relief algorithms within the single-channel (one microphone) case. the focal point is at the Most worthy methods, i.e., filtering ideas (in various domain names) and spectral enhancement equipment. the opposite target of Noise aid in Speech Processing is to derive a majority of these recognized suggestions in a rigorous approach and end up many basic and intuitive effects frequently taken without any consideration.
This booklet is mainly written for graduate scholars and examine engineers who paintings on noise relief for speech and audio functions and need to appreciate the sophisticated mechanisms in the back of each one method. Many new and engaging techniques are awarded during this textual content that we are hoping the readers will locate important and inspiring.
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Extra info for Noise reduction in speech processing
93) Proof. 88). The fullband output SNR for the KLE-domain approach is the ratio of two SPCCs depending on hl , l = 1, 2, . . , L and the sum of these two SPCCs is equal to 1. It can be checked that oSNR(h1:L ) > iSNR is equivalent to ρ2 (cx,F , cz ) > 2 ρ (cx , cy ). In this case, ρ2 (cv,F , cz ) < ρ2 (cv , cy ). 4 Summary In this chapter, we discussed many interesting properties of the SPCC in different domains. These fundamental properties establish relationships between the SPCC and the input and output SNRs.
46) since |ρ (V, VF ) |2 ≤ 1. 18. We have |ρ (VF , Y ) |2 = |ρ (V, VF ) |2 · |ρ (V, Y ) |2 . 50) Proof. Indeed |ρ (VF , Y ) |2 = = π −π π −π 2 |H(jω)| φv (ω)dω π −π π −π H(jω)φv (ω)dω π −π H(jω)φv (ω)dω 2 |H(jω)| φv (ω)dω π −π 2 φy (ω)dω 2 φv (ω)dω · 1 1 + iSNR = |ρ (V, VF ) |2 · |ρ (V, Y ) |2 . 19. 51) with equality when H(jω) = 1, ∀ω. Proof. 50) since |ρ (V, VF ) |2 ≤ 1. 20. 52) and oSNR(H) = |ρ (XF , Z) |2 . 53) Proof. 48). The fullband output SNR for the frequency-domain approach is the ratio of two fullband SPCCs and the sum of these two SPCCs is equal to 1.
Indeed, the human ear can do a pretty good job in judging the SNR values when we listen to some audio signals that are corrupted by additive noise with diﬀerent SNRs. The subband input SNR, which is a narrowband deﬁnition, is less obvious to understand in our context since we usually do not listen to narrowband signals. But this measure is relevant since it is closely related to the input SNR. The output SNR (computed after the processing is done in the time domain) and the fullband output SNR (computed after the processing is done in the transform domain) are also very reliable.