# Speech Enhancement Framework with Noise Suppression Using Block Principal Component Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Background and Motivation

#### 2.1. Principal Component Analysis

**Y**, for $i=1,\dots ,n$ with zero mean, and let $\mathsf{\Sigma}={\mathbf{Y}}^{\top}\mathbf{Y}=\mathrm{cov}\left({\mathbf{y}}^{i}\right)$ be the positive definite covariance matrix of size $p\times p$ estimated from the data itself. Eigen-value decomposition of the matrix $\Sigma $ (Gram matrix) is given by:

**V**is an orthonormal matrix, and each column vector ${\mathbf{Yv}}_{j}$ is the respective principal component. The vectors ${\mathbf{v}}_{j}$ are obtained by solving the following optimization problem:

**Y**into three factor matrices given by:

**V**contains the right singular vectors (the PC loadings), $\mathbf{U}$ contains left singular vectors, and $\Delta $ is a diagonal matrix with ordered singular values ${\delta}_{1}\ge {\delta}_{2}\ge \dots ,\ge {\delta}_{q}>0$. The PC vectors of $\mathbf{Y}$ are present in the matrix $\mathbf{U}\Delta $. Moreover,

**U**and

**V**are unitary, that is, ${\mathbf{U}}^{\top}\mathbf{U}={\mathbf{V}}^{\top}\mathbf{V}={\mathbf{I}}_{q}$. The SVD of a matrix

**Y**provides its closest rank-q matrix approximation ${\widehat{\mathbf{Y}}}_{q}$, where the closeness between

**Y**and ${\widehat{\mathbf{Y}}}_{q}$ is quantified by the squared Frobenius norm of their difference, that is, $\parallel \mathbf{Y}-{\widehat{\mathbf{Y}}}_{q}{\parallel}_{F}^{2}$.

#### 2.2. Motivation

**s**(n) be a single-channel clean time-domain speech signal,

**y**(n) be the noise-contaminated signal with a specific signal-to-noise ratio, and

**r**(n) be the corresponding noisy signal. Further assume that

**S**(k,m),

**Y**(k,m), and

**R**(k,m) are their respective short-time Fourier transform (STFT) matrices with size $1025\times 337$. The magnitude of these STFTs are shown in Figure 1. To generate these STFTs, we used Hamming window of size 1024, signal overlap of 64 samples (to obtain a dense matrix for visualization), and fast Fourier transform of size 2048. The sampling rate of the time-domain signal was $Fs=8000$ samples/s, leading to $\Delta f\approx 4$ Hz. The signal-to-noise ratio was kept at 0 dB. The speech signal contained the voice of a male speaker saying “The birch canoe slid on the smooth planks”, acquired from the NOIZEUS corpus [21]. The noise (interference) signal used here was ‘Babble’ noise, also from the NOIZEUS corpus. We show these STFTs in Figure 1.

**X**be the magnitude of an STFT of interest (

**S**or

**Y**). We take the SVD of

**X**according to (3). The resulting singular values are $diag(\Delta )$, principal components are $\mathbf{U}\Delta $, and PC loadings are $\mathbf{V}$. To visualize the spread of variance over various PCs, we show the first 200 (out of 337) singular values in Figure 2. From the slope of both curves, we can see that most of the data variance is captured by the few initial PCs. For the noise-free case, 20–40, and for the noisy case, 60–80 PCs are capturing most of the variance. Moreover, higher PCs (20 onwards) are more affected by the noise compared to lower ones; thus, ideally, getting rid of such noisy components should lead to reduction in the overall noise level.

## 3. Block Principal Component Analysis

**X**, with indices coming from

**b**. Then, we approximate ${\mathbf{X}}_{i}$ by first taking its SVD, $\mathbf{U}\Delta {\mathbf{V}}^{\top}=\mathrm{SVD}\left({\mathbf{X}}_{i}\right)$, and doing the following:

**X**(taking on (

**S**or

**Y**)). The singular values computed for each block ${\mathbf{X}}_{i}$ are shown in Figure 5a. Here, we can see that compared to Figure 2, the singular values in each block drop much more quickly; thus, keeping only a few components can lead to a better approximation. Here, we take $q=5$ components per block to generate ${\widehat{\mathbf{X}}}_{i}s$. For completeness, these $q=5$ singular values are also shown in Figure 5b. The approximated complete $\widehat{\mathbf{X}}$ is shown in Figure 6a, and the residual $(\widehat{\mathbf{X}}-\mathbf{X})$ is shown in Figure 6b. By comparing the noisy STFT

**X**(given in Figure 6c) and the approximated $\widehat{\mathbf{X}}$, we can see that the dominant features of our signal of interest (Figure 1a) are still present in the approximation, whereas most of the noise present in the 300–600 Hz range has been removed from the approximation and is delegated to the residual STFT. For comparison, the STFT of the noise-only signal is also shown in Figure 6d, and it is very close to our residual STFT.

## 4. Speech Enhancement Framework

- Let $\mathbf{y}\left(n\right)$ denote the time-domain noisy signal vector, $\mathbf{s}\left(n\right)$ be the clean speech signal, and $\mathbf{r}\left(n\right)$ represent noise. All signals at this step are real-valued and are related by:$$\mathbf{y}\left(n\right)=\mathbf{s}\left(n\right)+\mathbf{r}\left(n\right)$$
- Using Hamming window of appropriate size, segment $\mathbf{y}\left(n\right)$ vector into multiple frames with appropriate level of overlap. The resulting segmented noisy signal is given by:$$\mathbf{y}(n,m)=\mathbf{s}(n,m)+\mathbf{r}(n,m)$$
- Take the Fourier transform of the segmented noisy time-domain signal to get:$$\mathbf{Y}(k,m)=\mathbf{S}(k,m)+\mathbf{R}(k,m)$$
- Perform noise suppression on $\left|\mathbf{Y}\right(k,m\left)\right|$ via Block-PCA with block size b, retaining q components per block, according to the steps provided in Section 3, to get:$$\widehat{\mathbf{Y}}(k,m)=\mathrm{Block}-\mathrm{PCA}\left(\right)open="("\; close=")">\left|\mathbf{Y}\right(k,m\left)\right|,b,q$$
- Apply speech enhancement algorithm to the noise-suppressed magnitude spectra $\widehat{\mathbf{Y}}(k,m)$ to obtain the enhanced magnitude spectra $|\widehat{\mathbf{S}}(k,m)|$.
- Generate the complex-valued spectra by combining the enhanced magnitude spectra with the phase of the noisy spectrum as follows:$$\widehat{\mathbf{S}}(k,m)=\left|\widehat{\mathbf{S}}(k,m)\right|\phantom{\rule{0.277778em}{0ex}}{e}^{j{\mathbf{Y}}_{\theta}(k,m)}$$
- Take the inverse Fourier transform of $\widehat{\mathbf{S}}(k,m)$ to get $\widehat{\mathbf{s}}(n,m)$, apply the general overlap, and add the (OLA) method to obtain the enhanced time-domain signal $\widehat{\mathbf{s}}\left(n\right)$.

## 5. Experimental Evaluation

#### 5.1. PESQ and SSNR Performance for Male and Female Speakers under Babble Noise Contamination

#### 5.2. Performance Analysis under Different Noise Types Contamination with −6 dB SNR

#### 5.3. Performance Comparison for PESQ and SSNR over Multiple Noise Types and SNR Levels

#### 5.4. Performance Comparison for Babble and Exhibition Noise Types over Multiple Metrics and SNR Levels

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Magnitudes of short-time Fourier transforms of (

**a**) clean signal

**s**(n), (

**b**) noise-contaminated signal

**y**(n) with 0 dB signal-to-noise ratio, and (

**c**) noise

**r**(n).

**Figure 2.**The initial 200 singular values corresponding to the STFTs of clean (in solid blue) and noisy (in dash-dotted red) signals.

**Figure 3.**The principal components of the STFTs of clean (in solid blue) and noisy (in dash-dotted red) signals.

**Figure 4.**The PC loadings of the STFTs of clean (in solid blue) and noisy (in dash-dotted red) signals.

**Figure 5.**(

**a**) Singular values corresponding to every block, and (

**b**) the truncated singular values per block used for the approximated STFT $\widehat{\mathbf{X}}$ shown in Figure 6a.

**Figure 6.**The approximated STFT $\widehat{\mathbf{X}}$ of noisy STFT (

**a**) and the residual $(\widehat{\mathbf{X}}-\mathbf{X})$ (

**b**). For comparison, the noisy STFT

**X**=

**Y**and noise-only STFT from Figure 1 are reproduced in (

**c**,

**d**), respectively.

**Figure 7.**Average PESQ scores for male and female speakers under ‘Babble’ noise contamination over multiple SNR levels. (For interpretation of the bar colors, the reader is referred to the web version of this article).

**Figure 8.**Average SSNR improvement in dB with regard to the noise floor scores for male and female speakers under ‘Babble’ noise contamination over multiple SNR levels. (For interpretation of the bar colors, the reader is referred to the web version of this article).

**Figure 9.**PESQ scores for all noise types and all SNR noise levels. (For interpretation of the bar colors, the reader is referred to the web version of this article).

**Figure 10.**SSNR improvement in dB with regard to the noise floor scores for all noise types and SNR noise levels. (For interpretation of the bar colors, the reader is referred to the web version of this article).

Reference | Input SNR Range in dB |
---|---|

[3] | −3 dB onwards |

[5] | −5 dB onwards |

[6] | −15 dB onwards |

[10] | 0 dB onwards |

[11] | −5 dB onwards |

[20] | −3 dB onwards |

[22] | 1 dB onwards |

[23] | 0 dB onwards |

[24] | 0 dB onwards |

[25] | 0 dB onwards |

**Table 2.**Performance analysis for noise types ‘Babble’, ‘Exhibition’, and ‘Car’ with −6 dB SNR level. Best results are highlighted in

**bold**.

Babble | |||||||
---|---|---|---|---|---|---|---|

SNR dB | Method/Average Scores | PESQ | LLR | SSNR | Csig | Cbak | Covl |

−6 | Noisy | 1.39 | 1.03 | −7.15 | 2.11 | 1.30 | 1.62 |

MMSE-STSA | 1.30 | 1.16 | −4.77 | 1.77 | 1.26 | 1.37 | |

MMSE-STSA + BPCA | 1.78 | 1.09 | −4.24 | 2.12 | 1.51 | 1.75 | |

SSMB | 1.30 | 1.10 | −5.21 | 1.90 | 1.28 | 1.44 | |

SSMB + BPCA | 1.62 | 1.05 | −5.11 | 2.11 | 1.41 | 1.68 | |

ALMD | 1.13 | 1.21 | −4.97 | 1.69 | 1.19 | 1.29 | |

ALMD + BPCA | 1.53 | 1.15 | −4.66 | 1.97 | 1.38 | 1.60 | |

Exhibition | |||||||

SNR dB | Method/Average Scores | PESQ | LLR | SSNR | Csig | Cbak | Covl |

−6 | Noisy | 1.32 | 1.25 | −7.20 | 1.91 | 1.30 | 1.50 |

MMSE-STSA | 1.26 | 1.25 | −4.24 | 1.76 | 1.35 | 1.36 | |

MMSE-STSA + BPCA | 1.78 | 1.21 | −3.89 | 2.09 | 1.61 | 1.77 | |

SSMB | 1.24 | 1.24 | −4.78 | 1.79 | 1.33 | 1.37 | |

SSMB + BPCA | 1.42 | 1.22 | −4.61 | 1.93 | 1.44 | 1.53 | |

ALMD | 1.14 | 1.28 | −4.39 | 1.69 | 1.31 | 1.31 | |

ALMD + BPCA | 1.48 | 1.24 | −4.13 | 1.92 | 1.49 | 1.59 | |

Car | |||||||

SNR dB | Method/Average Scores | PESQ | LLR | SSNR | Csig | Cbak | Covl |

−6 | Noisy | 1.40 | 1.12 | −7.43 | 2.06 | 1.30 | 1.58 |

MMSE-STSA | 1.60 | 1.08 | −3.65 | 2.23 | 1.62 | 1.79 | |

MMSE-STSA + BPCA | 2.42 | 1.05 | −3.51 | 2.74 | 2.01 | 2.44 | |

SSMB | 1.36 | 1.11 | −4.66 | 2.03 | 1.56 | 1.56 | |

SSMB + BPCA | 1.50 | 1.08 | −4.54 | 2.15 | 1.68 | 1.68 | |

ALMD | 1.52 | 1.15 | −3.90 | 2.28 | 1.70 | 1.84 | |

ALMD + BPCA | 2.00 | 1.13 | −3.77 | 2.60 | 1.96 | 2.23 |

**Table 3.**All performance metric scores for noise type ‘Babble’ over multiple SNR levels. Best results are highlighted in

**bold**.

Babble | |||||||
---|---|---|---|---|---|---|---|

SNR dB | Method/Noises | PESQ | LLR | SSNR | Csig | Cbak | Covl |

−9 | Noisy | 1.30 | 1.11 | −8.23 | 1.92 | 1.20 | 1.49 |

MMSE-STSA | 1.06 | 1.25 | −5.72 | 1.46 | 1.10 | 1.14 | |

MMSE-STSA + BPCA | 1.86 | 1.17 | −5.06 | 1.99 | 1.45 | 1.72 | |

SSMB | 1.18 | 1.17 | −6.11 | 1.69 | 1.18 | 1.29 | |

SSMB + BPCA | 1.65 | 1.13 | −6.06 | 1.98 | 1.34 | 1.63 | |

ALMD | 0.87 | 1.99 | −7.78 | 1.39 | 1.03 | 1.06 | |

ALMD + BPCA | 1.50 | 1.93 | −7.42 | 1.80 | 1.29 | 1.52 | |

−6 | Noisy | 1.40 | 1.03 | −7.15 | 2.11 | 1.30 | 1.62 |

MMSE-STSA | 1.30 | 1.16 | −4.77 | 1.77 | 1.26 | 1.37 | |

MMSE-STSA + BPCA | 1.78 | 1.09 | −4.24 | 2.12 | 1.51 | 1.75 | |

SSMB | 1.30 | 1.10 | −5.21 | 1.90 | 1.28 | 1.44 | |

SSMB + BPCA | 1.62 | 1.05 | −5.11 | 2.11 | 1.41 | 1.68 | |

ALMD | 1.13 | 1.21 | −4.97 | 1.69 | 1.19 | 1.29 | |

ALMD + BPCA | 1.53 | 1.15 | −4.66 | 1.97 | 1.38 | 1.60 | |

−3 | Noisy | 1.54 | 0.95 | −5.85 | 2.35 | 1.46 | 1.80 |

MMSE-STSA | 1.60 | 1.07 | −3.80 | 2.12 | 1.51 | 1.69 | |

MMSE-STSA + BPCA | 1.80 | 1.02 | −3.45 | 2.31 | 1.65 | 1.89 | |

SSMB | 1.59 | 1.01 | −4.19 | 2.24 | 1.53 | 1.75 | |

SSMB + BPCA | 1.73 | 0.95 | −4.12 | 2.36 | 1.59 | 1.87 | |

ALMD | 1.47 | 0.98 | −3.59 | 2.14 | 1.55 | 1.71 | |

ALMD + BPCA | 1.64 | 0.92 | −3.39 | 2.30 | 1.64 | 1.87 | |

0 | Noisy | 1.74 | 0.86 | −4.35 | 2.64 | 1.71 | 2.08 |

MMSE-STSA | 1.86 | 0.98 | -2.81 | 2.46 | 1.77 | 2.01 | |

MMSE-STSA + BPCA | 1.95 | 0.93 | −2.57 | 2.57 | 1.84 | 2.11 | |

SSMB | 1.80 | 0.92 | −3.25 | 2.53 | 1.74 | 2.03 | |

SSMB + BPCA | 1.89 | 0.86 | −3.21 | 2.63 | 1.79 | 2.12 | |

ALMD | 1.77 | 0.79 | −2.33 | 2.53 | 1.85 | 2.09 | |

ALMD + BPCA | 1.86 | 0.74 | −2.19 | 2.64 | 1.91 | 2.19 | |

3 | Noisy | 1.92 | 0.76 | −2.70 | 2.93 | 1.96 | 2.33 |

MMSE-STSA | 2.09 | 0.88 | −1.80 | 2.79 | 2.01 | 2.31 | |

MMSE-STSA + BPCA | 2.14 | 0.84 | −1.59 | 2.88 | 2.06 | 2.39 | |

SSMB | 2.02 | 0.81 | −2.19 | 2.85 | 1.97 | 2.32 | |

SSMB + BPCA | 2.08 | 0.76 | −2.17 | 2.94 | 2.01 | 2.39 | |

ALMD | 2.19 | 0.67 | −0.80 | 2.92 | 2.12 | 2.45 | |

ALMD + BPCA | 2.25 | 0.62 | -0.68 | 3.01 | 2.17 | 2.53 | |

6 | Noisy | 2.11 | 0.65 | −0.91 | 3.22 | 2.22 | 2.59 |

MMSE-STSA | 2.28 | 0.78 | −0.82 | 3.09 | 2.22 | 2.58 | |

MMSE-STSA + BPCA | 2.33 | 0.74 | −0.63 | 3.17 | 2.27 | 2.65 | |

SSMB | 2.22 | 0.71 | −1.22 | 3.14 | 2.18 | 2.58 | |

SSMB + BPCA | 2.27 | 0.66 | −1.12 | 3.23 | 2.22 | 2.65 | |

ALMD | 2.45 | 0.51 | 0.63 | 3.26 | 2.37 | 2.76 | |

ALMD + BPCA | 2.50 | 0.47 | 0.77 | 3.34 | 2.42 | 2.83 |

**Table 4.**All performance metric scores for noise type ‘Exhibition’ over multiple SNR levels. Best results are highlighted in

**bold**.

Exhibition | |||||||
---|---|---|---|---|---|---|---|

−9 | Noisy | 1.17 | 1.32 | −8.27 | 1.71 | 1.18 | 1.35 |

MMSE-STSA | 1.20 | 1.33 | −4.94 | 1.60 | 1.26 | 1.30 | |

MMSE-STSA + BPCA | 1.99 | 1.30 | −4.61 | 2.05 | 1.61 | 1.84 | |

SSMB | 1.13 | 1.31 | −5.73 | 1.60 | 1.22 | 1.27 | |

SSMB + BPCA | 1.46 | 1.30 | −5.56 | 1.82 | 1.37 | 1.50 | |

ALMD | 1.02 | 1.37 | −4.86 | 1.49 | 1.14 | 1.16 | |

ALMD + BPCA | 1.58 | 1.36 | −4.61 | 1.82 | 1.39 | 1.54 | |

−6 | Noisy | 1.32 | 1.25 | −7.20 | 1.91 | 1.30 | 1.50 |

MMSE-STSA | 1.26 | 1.25 | −4.24 | 1.76 | 1.35 | 1.36 | |

MMSE-STSA + BPCA | 1.78 | 1.21 | −3.89 | 2.09 | 1.61 | 1.77 | |

SSMB | 1.24 | 1.24 | −4.78 | 1.79 | 1.33 | 1.37 | |

SSMB + BPCA | 1.42 | 1.22 | −4.61 | 1.93 | 1.44 | 1.53 | |

ALMD | 1.14 | 1.28 | −4.39 | 1.69 | 1.31 | 1.31 | |

ALMD + BPCA | 1.48 | 1.24 | −4.13 | 1.92 | 1.49 | 1.59 | |

−3 | Noisy | 1.47 | 1.17 | −5.89 | 2.15 | 1.48 | 1.70 |

MMSE-STSA | 1.48 | 1.14 | −3.42 | 2.08 | 1.55 | 1.63 | |

MMSE-STSA + BPCA | 1.73 | 1.11 | −3.14 | 2.24 | 1.69 | 1.84 | |

SSMB | 1.42 | 1.15 | −3.84 | 2.06 | 1.52 | 1.60 | |

SSMB + BPCA | 1.54 | 1.11 | −3.65 | 2.18 | 1.60 | 1.72 | |

ALMD | 1.55 | 1.17 | −2.75 | 2.17 | 1.63 | 1.72 | |

ALMD + BPCA | 1.73 | 1.13 | −2.52 | 2.31 | 1.74 | 1.88 | |

0 | Noisy | 1.61 | 1.07 | −4.39 | 2.41 | 1.69 | 1.91 |

MMSE-STSA | 1.72 | 1.04 | −2.54 | 2.38 | 1.77 | 1.92 | |

MMSE-STSA + BPCA | 1.81 | 1.01 | −2.28 | 2.45 | 1.83 | 2.00 | |

SSMB | 1.66 | 1.04 | −2.91 | 2.38 | 1.74 | 1.90 | |

SSMB + BPCA | 1.77 | 0.98 | −2.70 | 2.52 | 1.82 | 2.02 | |

ALMD | 1.84 | 1.06 | −1.42 | 2.53 | 1.92 | 2.06 | |

ALMD + BPCA | 1.94 | 1.01 | −1.18 | 2.64 | 1.99 | 2.17 | |

3 | Noisy | 1.78 | 0.96 | −2.72 | 2.69 | 1.93 | 2.15 |

MMSE-STSA | 1.96 | 0.95 | −1.58 | 2.69 | 1.99 | 2.21 | |

MMSE-STSA + BPCA | 2.02 | 0.91 | −1.33 | 2.75 | 2.03 | 2.27 | |

SSMB | 1.89 | 0.92 | −1.98 | 2.70 | 1.96 | 2.19 | |

SSMB + BPCA | 2.02 | 0.85 | −1.67 | 2.85 | 2.05 | 2.33 | |

ALMD | 2.11 | 0.91 | 0.02 | 2.88 | 2.17 | 2.40 | |

ALMD + BPCA | 2.20 | 0.85 | 0.30 | 2.99 | 2.24 | 2.50 | |

6 | Noisy | 1.97 | 0.84 | -0.92 | 2.98 | 2.18 | 2.41 |

MMSE-STSA | 2.19 | 0.84 | −0.61 | 3.00 | 2.21 | 2.50 | |

MMSE-STSA + BPCA | 2.23 | 0.82 | −0.39 | 3.04 | 2.25 | 2.54 | |

SSMB | 2.11 | 0.80 | −1.01 | 3.01 | 2.17 | 2.47 | |

SSMB + BPCA | 2.23 | 0.74 | −0.66 | 3.17 | 2.26 | 2.61 | |

ALMD | 2.44 | 0.75 | 1.49 | 3.25 | 2.47 | 2.71 | |

ALMD + BPCA | 2.52 | 0.71 | 1.78 | 3.34 | 2.53 | 2.80 |

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## Share and Cite

**MDPI and ACS Style**

Alsheibi, A.Z.; Valavanis, K.P.; Iqbal, A.; Aman, M.N.
Speech Enhancement Framework with Noise Suppression Using Block Principal Component Analysis. *Acoustics* **2022**, *4*, 441-459.
https://doi.org/10.3390/acoustics4020027

**AMA Style**

Alsheibi AZ, Valavanis KP, Iqbal A, Aman MN.
Speech Enhancement Framework with Noise Suppression Using Block Principal Component Analysis. *Acoustics*. 2022; 4(2):441-459.
https://doi.org/10.3390/acoustics4020027

**Chicago/Turabian Style**

Alsheibi, Abdullah Zaini, Kimon P. Valavanis, Asif Iqbal, and Muhammad Naveed Aman.
2022. "Speech Enhancement Framework with Noise Suppression Using Block Principal Component Analysis" *Acoustics* 4, no. 2: 441-459.
https://doi.org/10.3390/acoustics4020027