Spectral Processing

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S. R. M. Prasanna - One of the best experts on this subject based on the ideXlab platform.

  • Quality Evaluation of Combined Temporal and Spectral Processing for Hearing Impaired
    2019 IEEE 16th India Council International Conference (INDICON), 2019
    Co-Authors: Hemangi Shinde, A. M. Sapkal, Aishwarys Phatak, C. M. Vikram, S. R. M. Prasanna
    Abstract:

    This paper investigates the effectiveness of combined temporal and Spectral enhancement methods for the hearing impaired listeners. The temporal enhancement algorithm involves the identification and enhancement of excitation source specific regions at gross and fine levels using linear prediction residual. The temporal enhancement algorithm is combined with four different Spectral enhancement algorithms, namely, Spectral subtraction, multi-band Spectral subtraction, minimum mean square error short-time Spectral amplitude (MMSE-STSA) and minimum mean square error log-Spectral amplitude (MMSE-LSA). The temporal, Spectral and combined temporal-Spectral enhancement algorithms are applied on the speech signal, degraded under cafeteria, train, traffic and station noises for four SNR scenarios namely, -5, 0, 5 and 10 dB. The quality of enhanced speech signals is evaluated on both normal and hearing-impaired listeners. The combined temporal Spectral Processing algorithm showed significant improvement over individual Spectral and temporal methods and for hearing impaired listeners the results are almost comparable with that of ideal binary masking approach for the cafeteria noise case.

  • Enhancement of noisy speech by temporal and Spectral Processing
    Speech Communication, 2011
    Co-Authors: P. Krishnamoorthy, S. R. M. Prasanna
    Abstract:

    This paper presents a noisy speech enhancement method by combining linear prediction (LP) residual weighting in the time domain and Spectral Processing in the frequency domain to provide better noise suppression as well as better enhancement in the speech regions. The noisy speech is initially processed by the excitation source (LP residual) based temporal Processing that involves identifying and enhancing the excitation source based speech-specific features present at the gross and fine temporal levels. The gross level features are identified by estimating the following speech parameters: sum of the peaks in the discrete Fourier transform (DFT) spectrum, smoothed Hilbert envelope of the LP residual and modulation spectrum values, all from the noisy speech signal. The fine level features are identified using the knowledge of the instants of significant excitation. A weight function is derived from the gross and fine weight functions to obtain the temporally processed speech signal. The temporally processed speech is further subjected to Spectral domain Processing. Spectral Processing involves estimation and removal of degrading components, and also identification and enhancement of speech-specific Spectral components. The proposed method is evaluated using different objective and subjective quality measures. The quality measures show that the proposed combined temporal and Spectral Processing method provides better enhancement, compared to either temporal or Spectral Processing alone.

  • Reverberant Speech Enhancement by Temporal and Spectral Processing
    IEEE Transactions on Audio Speech and Language Processing, 2009
    Co-Authors: P. Krishnamoorthy, S. R. M. Prasanna
    Abstract:

    This paper presents an approach for the enhancement of reverberant speech by temporal and Spectral Processing. Temporal Processing involves identification and enhancement of high signal-to-reverberation ratio (SRR) regions in the temporal domain. Spectral Processing involves removal of late reverberant components in the Spectral domain. First, the Spectral subtraction-based Processing is performed to eliminate the late reverberant components, and then the Spectrally processed speech is further subjected to the excitation source information-based temporal Processing to enhance the high SRR regions. The objective measures segmental SRR and log Spectral distance are computed for different cases, namely, reverberant, Spectral processed, temporal processed, and combined temporal and Spectral processed speech signals. The quality of the speech signal that is processed by the temporal and Spectral Processing is significantly enhanced compared to the reverberant speech as well as the signals that are processed by the individual temporal and Spectral Processing methods.

  • Temporal and Spectral Processing of Degraded Speech
    2008 16th International Conference on Advanced Computing and Communications, 2008
    Co-Authors: P. Krishnamoorthy, S. R. M. Prasanna
    Abstract:

    This paper proposes the combined temporal and Spectral Processing approach for enhancement of degraded speech. Temporal Processing refers to the Processing of excitation source information in the temporal domain which mainly involves identification and enhancement of high signal-to-noise ratio (SNR) regions in the time domain representation of the degraded speech signal. Similarly Spectral Processing involves identification and suppression of degraded components in the frequency domain representation of the degraded speech signal.

P. Krishnamoorthy - One of the best experts on this subject based on the ideXlab platform.

  • Enhancement of noisy speech by temporal and Spectral Processing
    Speech Communication, 2011
    Co-Authors: P. Krishnamoorthy, S. R. M. Prasanna
    Abstract:

    This paper presents a noisy speech enhancement method by combining linear prediction (LP) residual weighting in the time domain and Spectral Processing in the frequency domain to provide better noise suppression as well as better enhancement in the speech regions. The noisy speech is initially processed by the excitation source (LP residual) based temporal Processing that involves identifying and enhancing the excitation source based speech-specific features present at the gross and fine temporal levels. The gross level features are identified by estimating the following speech parameters: sum of the peaks in the discrete Fourier transform (DFT) spectrum, smoothed Hilbert envelope of the LP residual and modulation spectrum values, all from the noisy speech signal. The fine level features are identified using the knowledge of the instants of significant excitation. A weight function is derived from the gross and fine weight functions to obtain the temporally processed speech signal. The temporally processed speech is further subjected to Spectral domain Processing. Spectral Processing involves estimation and removal of degrading components, and also identification and enhancement of speech-specific Spectral components. The proposed method is evaluated using different objective and subjective quality measures. The quality measures show that the proposed combined temporal and Spectral Processing method provides better enhancement, compared to either temporal or Spectral Processing alone.

  • Application of combined temporal and Spectral Processing methods for speaker recognition under noisy, reverberant or multi-speaker environments
    Sadhana, 2009
    Co-Authors: P. Krishnamoorthy, S. R. Mahadeva Prasanna
    Abstract:

    This paper presents an experimental evaluation of the combined temporal and Spectral Processing methods for speaker recognition task under noise, reverberation or multi-speaker environments. Automatic speaker recognition system gives good performance in controlled environments. Speech recorded in real environments by distant microphones is degraded by factors like background noise, reverberation and interfering speakers. This degradation strongly affects the performance of the speaker recognition system. Combined temporal and Spectral Processing (TSP) methods proposed in our earlier study are used for pre-Processing to improve the speaker-specific features and hence the speaker recognition performance. Different types of degradation like background noise, reverberation and interfering speaker are considered for evaluation. The evaluation is carried out for the individual temporal Processing, Spectral Processing and the combined TSP method. The experimental results show that the combined TSP methods give relatively higher recognition performance compared to either temporal or Spectral Processing alone.

  • Reverberant Speech Enhancement by Temporal and Spectral Processing
    IEEE Transactions on Audio Speech and Language Processing, 2009
    Co-Authors: P. Krishnamoorthy, S. R. M. Prasanna
    Abstract:

    This paper presents an approach for the enhancement of reverberant speech by temporal and Spectral Processing. Temporal Processing involves identification and enhancement of high signal-to-reverberation ratio (SRR) regions in the temporal domain. Spectral Processing involves removal of late reverberant components in the Spectral domain. First, the Spectral subtraction-based Processing is performed to eliminate the late reverberant components, and then the Spectrally processed speech is further subjected to the excitation source information-based temporal Processing to enhance the high SRR regions. The objective measures segmental SRR and log Spectral distance are computed for different cases, namely, reverberant, Spectral processed, temporal processed, and combined temporal and Spectral processed speech signals. The quality of the speech signal that is processed by the temporal and Spectral Processing is significantly enhanced compared to the reverberant speech as well as the signals that are processed by the individual temporal and Spectral Processing methods.

  • Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review
    IETE Technical Review, 2009
    Co-Authors: P. Krishnamoorthy, S. R. Mahadeva Prasanna
    Abstract:

    AbstractThis paper presents an overview of several most commonly used temporal and Spectral Processing methods for the enhancement of degraded speech. Three major sources of degradation, namely, background noise, reverberation and speech from the competing speakers, have been considered. Temporal Processing refers to Processing the degraded speech in the time domain, for enhancing the speech components. Spectral Processing refers to Processing the degraded speech in the frequency domain. After the review, the paper concludes with a summation that considers the possibility of combined Temporal and Spectral Processing (TSP) approach for the enhancement of degraded speech.

  • Temporal and Spectral Processing of Degraded Speech
    2008 16th International Conference on Advanced Computing and Communications, 2008
    Co-Authors: P. Krishnamoorthy, S. R. M. Prasanna
    Abstract:

    This paper proposes the combined temporal and Spectral Processing approach for enhancement of degraded speech. Temporal Processing refers to the Processing of excitation source information in the temporal domain which mainly involves identification and enhancement of high signal-to-noise ratio (SNR) regions in the time domain representation of the degraded speech signal. Similarly Spectral Processing involves identification and suppression of degraded components in the frequency domain representation of the degraded speech signal.

Hansgunter Hirsch - One of the best experts on this subject based on the ideXlab platform.

Hynek Hermansky - One of the best experts on this subject based on the ideXlab platform.

S. R. Mahadeva Prasanna - One of the best experts on this subject based on the ideXlab platform.

  • Enhancement of cleft palate speech using temporal and Spectral Processing
    Speech Communication, 2020
    Co-Authors: Protima Nomo Sudro, S. R. Mahadeva Prasanna
    Abstract:

    Abstract The speech of the individuals with cleft palate (CP) is generally characterized by the presence of abnormal nasal resonances during the production of voiced sounds, primarily in vowels, and is called hypernasality. Hypernasality is present in more than 50% of the individuals with CP, and it often results in degraded speech, both in quality and intelligibility. The current work describes the signal Processing based enhancement of CP speech, where specifically hypernasal speech modification is addressed. The hypernasal speech’s residual and vocal tract system characteristics are analyzed using an extended weighted linear prediction (XLP) method. The enhancement is performed for three different variants: XLP residual weighting in the time domain, Gaussian mixture model-based Spectral conversion in the frequency domain, and combined modification of the XLP residual and vocal tract system characteristics. The modified hypernasal speech achieved by the proposed method is evaluated using different objective and subjective measures for the vowel /a/, /i/, and /u/. The evaluation results indicate that the combination of XLP residual and vocal tract system characteristics modification yields better results than XLP residual or vocal tract system characteristics modification alone.

  • Application of combined temporal and Spectral Processing methods for speaker recognition under noisy, reverberant or multi-speaker environments
    Sadhana, 2009
    Co-Authors: P. Krishnamoorthy, S. R. Mahadeva Prasanna
    Abstract:

    This paper presents an experimental evaluation of the combined temporal and Spectral Processing methods for speaker recognition task under noise, reverberation or multi-speaker environments. Automatic speaker recognition system gives good performance in controlled environments. Speech recorded in real environments by distant microphones is degraded by factors like background noise, reverberation and interfering speakers. This degradation strongly affects the performance of the speaker recognition system. Combined temporal and Spectral Processing (TSP) methods proposed in our earlier study are used for pre-Processing to improve the speaker-specific features and hence the speaker recognition performance. Different types of degradation like background noise, reverberation and interfering speaker are considered for evaluation. The evaluation is carried out for the individual temporal Processing, Spectral Processing and the combined TSP method. The experimental results show that the combined TSP methods give relatively higher recognition performance compared to either temporal or Spectral Processing alone.

  • Temporal and Spectral Processing Methods for Processing of Degraded Speech: A Review
    IETE Technical Review, 2009
    Co-Authors: P. Krishnamoorthy, S. R. Mahadeva Prasanna
    Abstract:

    AbstractThis paper presents an overview of several most commonly used temporal and Spectral Processing methods for the enhancement of degraded speech. Three major sources of degradation, namely, background noise, reverberation and speech from the competing speakers, have been considered. Temporal Processing refers to Processing the degraded speech in the time domain, for enhancing the speech components. Spectral Processing refers to Processing the degraded speech in the frequency domain. After the review, the paper concludes with a summation that considers the possibility of combined Temporal and Spectral Processing (TSP) approach for the enhancement of degraded speech.

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