A Study of Noise PSD Estimators for Single Channel Speech Enhancement

Aiming at the shortcomings of traditional broadcast transmitter noise test methods, such as low efficiency, inconvenient data storage, and high requirements for testers, a dynamic online test method for transmitter noise is proposed. The principle of system composition and test method is given. The transmitter noise is real-time changing. The Voice Active Detection (VAD) noise estimation algorithm cannot track the transmitter noise change in real time. This paper proposes a combined noise estimation algorithm for VAD and dynamic estimation. By setting the threshold of the double-threshold VAD detection to be low, it can accurately detect the silent segment. The silent segment is used as a noise signal for noise estimation. For the nonsilent segment detected by the VAD, a minimum value search dynamic spectrum estimation algorithm based on the existence probability of the speech (IMCRA) is used for noise estimation. Transmitter noise is measured by calculating the noise figure (NF).The test method collects the input and output data of the transmitter in real time, which has better accuracy and real-time performance, and the feasibility of the method is verified by experimental simulation.

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“…represents the conditional probability of the presence of a speech signal, and equation (17) can be simplified to…”

Section: Noise Estimation Algorithm Based On Dynamic Spectrum Estimationmentioning

confidence: 99%

A New Online Dynamic Testing Method for Noise of Broadcast Transmitter

Zhou

Wang

2020

Wireless Communications and Mobile Computing

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“…This paper has focused on motivating and deriving the proposed noise PSD estimator. Therefore, there is only a limited space left to provide evidence for that the fundamental principle works, but we have a more thorough evaluation in [25]. Here, we consider two different experiments.…”

Section: Evalutationmentioning

confidence: 99%

Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

Nielsen¹,

Kavalekalam²,

Christensen³

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in nonstationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estimators in terms of tracking speed and estimation accuracy.

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“…In [12], the authors introduced a model-based noise PSD estimator by applying a statistical model to the speech and noise signals. The proposed noise PSD estimator is able to include prior spectral information about speech and different types of non-stationary noise [13]. Based on the estimated noise PSDs, we apply the GLRT to obtain the global decision.…”

Section: Introductionmentioning

confidence: 99%

“…For noise PSD tracking, the well-known MMSE [9], [10] method and the MS [11] method work well with stationary noise. For non-stationary noise, however, they have limited performance [13]. In this section, we briefly summarize a model-based noise PSD estimation method which are able to track non-stationary noise, such as babble noise.…”

mentioning

confidence: 99%