[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Fan et al., 2007 - Google Patents

Speech noise estimation using enhanced minima controlled recursive averaging

Fan et al., 2007

View PDF
Document ID
10914872441954913213
Author
Fan N
Rosca J
Balan R
Publication year
Publication venue
2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07

External Links

Snippet

Accurate noise power spectrum estimation in a noisy speech signal is a key challenge problem in speech enhancement. One state-of-the-art approach is the minima controlled recursive averaging (MCRA). This paper presents an enhanced MCRA algorithm (EMCRA) …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02168—Noise filtering characterised by the method used for estimating noise the estimation exclusively taking place during speech pauses
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters
    • G10L25/09—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters the extracted parameters being zero crossing rates
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters
    • G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/78—Detection of presence or absence of voice signals
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech

Similar Documents

Publication Publication Date Title
CN103456310B (en) Transient noise suppression method based on spectrum estimation
CN105023572A (en) Noised voice end point robustness detection method
Fan et al. Speech noise estimation using enhanced minima controlled recursive averaging
CN110349598A (en) A kind of end-point detecting method under low signal-to-noise ratio environment
CN105575406A (en) Noise robustness detection method based on likelihood ratio test
Islam et al. Speech enhancement based on a modified spectral subtraction method
Zhang et al. A novel fast nonstationary noise tracking approach based on MMSE spectral power estimator
van Hout et al. A novel approach to soft-mask estimation and log-spectral enhancement for robust speech recognition
Gupta et al. Speech enhancement using MMSE estimation and spectral subtraction methods
KR100784456B1 (en) Voice Enhancement System using GMM
May et al. Generalization of supervised learning for binary mask estimation
Joder et al. Integrating noise estimation and factorization-based speech separation: A novel hybrid approach
Islam et al. Speech enhancement based on noise compensated magnitude spectrum
Farahani et al. Robust feature extraction of speech via noise reduction in autocorrelation domain
Shen et al. A priori SNR estimator based on a convex combination of two DD approaches for speech enhancement
Ghoreishi et al. A hybrid speech enhancement system based on HMM and spectral subtraction
Wang et al. Speech enhancement based on perceptually motivated guided spectrogram filtering
Win et al. Speech enhancement techniques for noisy speech in real world environments
Sanam et al. A DCT-based noisy speech enhancement method using teager energy operator
Sunnydayal et al. Speech enhancement using sub-band wiener filter with pitch synchronous analysis
Seyedin et al. Robust MVDR-based feature extraction for speech recognition
Wu et al. Robust speech recognition by selecting mel-filter banks
Guo et al. Denoising Algorithm of Environmental Sound Fused NMF and OMLSA in Non-Stationary Noise Environment
Islam et al. Enhancement of noisy speech based on decision-directed Wiener approach in perceptual wavelet packet domain
Benaroya et al. Experiments in audio source separation with one sensor for robust speech recognition