Speech Enhancement of Mobile Devices Based on the Integration of a Dual Microphone Array and a Background Noise Elimination Algorithm

Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions. ASR systems are widely used in smart appliances, smart homes, and biometric systems. Signal processing and machine learning techniques are incorporated to recognize speech. However, traditional systems have low performance due to a noisy environment. In addition to this, accents and local differences negatively affect the ASR system’s performance while analyzing speech signals. A precise speech recognition system was developed to improve the system performance to overcome these issues. This paper uses speech information from jim-schwoebel voice datasets processed by Mel-frequency cepstral coefficients (MFCCs). The MFCC algorithm extracts the valuable features that are used to recognize speech. Here, a sparse auto-encoder (SAE) neural network is used to classify the model, and the hidden Markov model (HMM) is used to decide on the speech recognition. The network performance is optimized by applying the Harris Hawks optimization (HHO) algorithm to fine-tune the network parameter. The fine-tuned network can effectively recognize speech in a noisy environment.

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“…, q n } and the probability value of p(q i ) of Q. Then, according to Q, every subsignal entropy value is estimated using Equation (13).…”

Section: 𝑐(𝑛) → (𝐼 |𝑓 | 𝑓 |𝑓 |𝑓 )mentioning

confidence: 99%

“…Noise is important for malware attacks in Image-based classification [12]. Especially in offices, public spaces, and urban outdoors, noise will be one of the significant challenges in speech recognition [13]. Noise occurred due to variations in locales and the accents of speaker's speech.…”

Section: Introductionmentioning

confidence: 99%

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Ali¹,

Jaber

Khalil

et al. 2022

Applied Sciences

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“…Spatial cues between multi-channel signals such as inter-channel time difference (or inter-channel phase difference) and inter-channel level difference can indicate the location of the speech source. These spatial characteristics have been shown to be particularly beneficial when combined with spectral characteristics over the frequency domain in several fields, such as source separation, speech enhancement, and voice activity detection [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Unfortunately, these spatial features are typically extracted in the frequency domain using STFT, making it difficult to integrate perfectly using the time domain method.…”

Section: Proposed Multi-channel Cross-tower With Attention Mechanimentioning

confidence: 99%

Multi-TALK: Multi-Microphone Cross-Tower Network for Jointly Suppressing Acoustic Echo and Background Noise

Park

Chang

2020

Sensors

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In this paper, we propose a multi-channel cross-tower with attention mechanisms in latent domain network (Multi-TALK) that suppresses both the acoustic echo and background noise. The proposed approach consists of the cross-tower network, a parallel encoder with an auxiliary encoder, and a decoder. For the multi-channel processing, a parallel encoder is used to extract latent features of each microphone, and the latent features including the spatial information are compressed by a 1D convolution operation. In addition, the latent features of the far-end are extracted by the auxiliary encoder, and they are effectively provided to the cross-tower network by using the attention mechanism. The cross tower network iteratively estimates the latent features of acoustic echo and background noise in each tower. To improve the performance at each iteration, the outputs of each tower are transmitted as the input for the next iteration of the neighboring tower. Before passing through the decoder, to estimate the near-end speech, attention mechanisms are further applied to remove the estimated acoustic echo and background noise from the compressed mixture to prevent speech distortion by over-suppression. Compared to the conventional algorithms, the proposed algorithm effectively suppresses the acoustic echo and background noise and significantly lowers the speech distortion.

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“…More recently, Fu et al [3] used a minimum variance distortionless response spatial filter for noise reduction, which was shown to be more robust than the PLD method in [2] against different sensitivities of two microphones. Other related studies include [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Speech Enhancement for Mobile Communication Based on Dual-Channel Complex Spectral Mapping

Tan

Zhang

Wang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Speech quality and intelligibility can be severely degraded by background noise in mobile communication. In order to attenuate background noise, speech enhancement systems have been integrated into mobile phones, and a microphone array is typically deployed to improve the enhancement performance. This paper proposes a novel approach to real-time speech enhancement for dual-microphone mobile phones. Our approach employs a causal densely-connected convolutional recurrent network to perform dual-channel complex spectral mapping. We apply a structured pruning technique for compressing the model without significantly affecting the enhancement performance. This leads to a real-time enhancement system for on-device processing. Evaluation results show that the proposed approach substantially advances the performance of an earlier approach to dual-channel speech enhancement for mobile communication.

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Speech Enhancement of Mobile Devices Based on the Integration of a Dual Microphone Array and a Background Noise Elimination Algorithm

Cited by 7 publications

References 17 publications

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Multi-TALK: Multi-Microphone Cross-Tower Network for Jointly Suppressing Acoustic Echo and Background Noise

Real-Time Speech Enhancement for Mobile Communication Based on Dual-Channel Complex Spectral Mapping

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