• Title, Summary, Keyword: Noisy environments

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Analysis of Pre-Processing Methods for Music Information Retrieval in Noisy Environments using Mobile Devices

  • Kim, Dae-Jin;Koo, Ddeo-Ol-Ra
    • International Journal of Contents
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    • v.8 no.2
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    • pp.1-6
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    • 2012
  • Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

Adaptive Band Selection for Robust Speech Detection In Noisy Environments

  • Ji Mikyong;Suh Youngjoo;Kim Hoirin
    • MALSORI
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    • no.50
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    • pp.85-97
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    • 2004
  • One of the important problems in speech recognition is to accurately detect the existence of speech in adverse environments. The speech detection problem becomes severer when recognition systems are used over the telephone network, especially in a wireless network and a noisy environment. In this paper, we propose a robust speech detection algorithm, which detects speech boundaries accurately by selecting useful bands adaptively to noisy environments. The bands where noises are mainly distributed, so called, noise-centric bands are introduced. In this paper, we compare two different speech detection algorithms with the proposed algorithm, and evaluate them on noisy environments. The experimental results show the excellence of the proposed speech detection algorithm.

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Voice Activity Detection Algorithm Using Speech Periodicity and QSNR in Noisy Environment (음성의 주기성과 QSNR을 이용한 잡음환경에서의 음성검출 알고리즘)

  • Jeong, Ju-Hyun;Song, Hwa-Jeon;Kim, Hyung-Soon
    • Proceedings of the KSPS conference
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    • pp.59-62
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    • 2005
  • Voice activity detection (VAD) is important in many areas of speech processing technology. Speech/nonspeech discrimination in noisy environments is a difficult task because the feature parameters used for the VAD are sensitive to the surrounding environments. Thus the VAD performance is severely degraded at low signal-to-noise ratios (SNRs). In this paper, a new VAD algorithm is proposed based on the degree of voicing and Quantile SNR (QSNR). These two feature parameters are more robust than other features such as energy and spectral entropy in noisy environments. The effectiveness of proposed algorithm is evaluated under the diverse noisy environments in the Aurora2 DB. According to out experiment, the proposed VAD outperforms the ETSI Advanced Frontend VAD.

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Noise Estimation based on Standard Deviation and Sigmoid Function Using a Posteriori Signal to Noise Ratio in Nonstationary Noisy Environments

  • Lee, Soo-Jeong;Kim, Soon-Hyob
    • International Journal of Control, Automation, and Systems
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    • v.6 no.6
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    • pp.818-827
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    • 2008
  • In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

Energy Feature Normalization for Robust Speech Recognition in Noisy Environments

  • Lee, Yoon-Jae;Ko, Han-Seok
    • Speech Sciences
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    • v.13 no.1
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    • pp.129-139
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    • 2006
  • In this paper, we propose two effective energy feature normalization methods for robust speech recognition in noisy environments. In the first method, we estimate the noise energy and remove it from the noisy speech energy. In the second method, we propose a modified algorithm for the Log-energy Dynamic Range Normalization (ERN) method. In the ERN method, the log energy of the training data in a clean environment is transformed into the log energy in noisy environments. If the minimum log energy of the test data is outside of a pre-defined range, the log energy of the test data is also transformed. Since the ERN method has several weaknesses, we propose a modified transform scheme designed to reduce the residual mismatch that it produces. In the evaluation conducted on the Aurora2.0 database, we obtained a significant performance improvement.

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Confidence Measure for Utterance Verification in Noisy Environments (잡음 환경에서의 인식 거부 성능 향상을 위한 신뢰 척도)

  • Park, Jeong-Sik;Oh, Yung-Hwan
    • Proceedings of the KSPS conference
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    • pp.3-6
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    • 2006
  • This paper proposes a confidence measure employed for utterance verification in noisy environments. Most of conventional approaches estimate the proper threshold of confidence measure and apply the value to utterance rejection in recognition process. As such, their performance may degrade for noisy speech since the threshold can be changed in noisy environments. This paper presents further robust confidence measure based on the multi-pass confidence measure. The isolated word recognition based experimental results demonstrate that the proposed method outperforms conventional approaches as utterance verifier.

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Performance Enhancement of Speaker Identification in Noisy Environments by Optimization Membership Function Based on Particle Swarm (Particle Swarm 기반 최적화 멤버쉽 함수에 의한 잡음 환경에서의 화자인식 성능향상)

  • Min, So-Hee;Song, Min-Gyu;Na, Seung-You;Kim, Jin-Young
    • Speech Sciences
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    • v.14 no.2
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    • pp.105-114
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    • 2007
  • The performance of speaker identifier is severely degraded in noisy environments. A study suggested the concept of observation membership for enhancing performances of speaker identifier with noisy speech [1]. The method scaled observation probabilities of input speech by observation identification values decided by SNR. In the paper [1], the authors suggested heuristic parameter values for membership function. In this paper we attempt to apply particle swarm optimization (PSO) for obtaining the optimal parameters for speaker identification in noisy environments. With the speaker identification experiments using the ETRI database we prove that the optimization approach can yield better performance than using only the original membership function.

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Enhancement of Rejection Performance using the PSO-NCM in Noisy Environment (잡음 환경하에서의 PSO-NCM을 이용한 거절기능 성능 향상)

  • Kim, Byoung-Don;Song, Min-Gyu;Choi, Seung-Ho;Kim, Jin-Young
    • Speech Sciences
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    • v.15 no.4
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    • pp.85-96
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    • 2008
  • Automatic speech recognition has severe performance degradation under noisy environments. To cope with the noise problem, many methods have been proposed. Most of them focused on noise-robust features or model adaptation. However, researchers have overlooked utterance verification (UV) under noisy environments. In this paper we discuss UV problems based on the normalized confidence measure. First, we show that UV performance is also degraded in noisy environments with the experiments of an isolated word recognition. Then we observe how the degradation of UV performances is suffered. Based on the UV experiments we propose a modeling method of the statistics of phone confidences using sigmoid functions. For obtaining the parameters of the sigmoidal models, the particle swarm optimization (PSO) is adopted. The proposed method improves 20% rejection performance. Our experimental results show that the PSO-NCM can apply noise speech recognition successfully.

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Adaptive Threshold for Speech Enhancement in Nonstationary Noisy Environments (비정상 잡음환경에서 음질향상을 위한 적응 임계 치 알고리즘)

  • Lee, Soo-Jeong;Kim, Sun-Hyob
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.7
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    • pp.386-393
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    • 2008
  • This paper proposes a new approach for speech enhancement in highly nonstationary noisy environments. The spectral subtraction (SS) is a well known technique for speech enhancement in stationary noisy environments. However, in real world, noise is mostly nonstationary. The proposed method uses an auto control parameter for an adaptive threshold to work well in highly nonstationary noisy environments. Especially, the auto control parameter is affected by a linear function associated with an a posteriori signal to noise ratio (SNR) according to the increase or the decrease of the noise level. The proposed algorithm is combined with spectral subtraction (SS) using a hangover scheme (HO) for speech enhancement. The performances of the proposed method are evaluated ITU-T P.835 signal distortion (SIG) and the segment signal to-noise ratio (SNR) in various and highly nonstationary noisy environments and is superior to that of conventional spectral subtraction (SS) using a hangover (HO) and SS using a minimum statistics (MS) methods.

A Noise Reduction Method Combined with HMM Composition for Speech Recognition in Noisy Environments

  • Shen, Guanghu;Jung, Ho-Youl;Chung, Hyun-Yeol
    • IEMEK Journal of Embedded Systems and Applications
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    • v.3 no.1
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    • pp.1-7
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    • 2008
  • In this paper, a MSS-NOVO method that combines the HMM composition method with a noise reduction method is proposed for speech recognition in noisy environments. This combined method starts with noise reduction with modified spectral subtraction (MSS) to enhance the input noisy speech, then the noise and voice composition (NOVO) method is applied for making noise adapted models by using the noise in the non-utterance regions of the enhanced noisy speech. In order to evaluate the effectiveness of our proposed method, we compare MSS-NOVO method with other methods, i.e., SS-NOVO, MWF-NOVO. To set up the noisy speech for test, we add White noise to KLE 452 database with different SNRs range from 0dB to 15dB, at 5dB intervals. From the tests, MSS-NOVO method shows average improvement of 66.5% and 13.6% compared with the existing SS-NOVO method and MWF-NOVO method, respectively. Especially our proposed MSS-NOVO method shows a big improvement at low SNRs.

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