An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.

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YIN, a fundamental frequency estimator for speech and music

In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve the intelligibility of noisy unprocessed speech. In this paper, a short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time-frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments. In general, STOI showed better correlation with speech intelligibility compared to five other reference objective intelligibility models. In contrast to other conventional intelligibility models which tend to rely on global statistics across entire sentences, STOI is based on shorter time segments (386 ms). Experiments indeed show that it is beneficial to take segment lengths of this order into account. In addition, a free Matlab implementation is provided.

An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech

福祉関連職におけるMaslach Burnout Inventoryの因子構造の比較

(NOTE: Each chapter begins with an introduction and concludes with a Summary, Exercises and Bibliography.) 1. Introduction. Discrete-Time Speech Signal Processing. The Speech Communication Pathway. Analysis/Synthesis Based on Speech Production and Perception. Applications. Outline of Book. 2. A Discrete-Time Signal Processing Framework. Discrete-Time Signals. Discrete-Time Systems. Discrete-Time Fourier Transform. Uncertainty Principle. z-Transform. LTI Systems in the Frequency Domain. Properties of LTI Systems. Time-Varying Systems. Discrete-Fourier Transform. Conversion of Continuous Signals and Systems to Discrete Time. 3. Production and Classification of Speech Sounds. Anatomy and Physiology of Speech Production. Spectrographic Analysis of Speech. Categorization of Speech Sounds. Prosody: The Melody of Speech. Speech Perception. 4. Acoustics of Speech Production. Physics of Sound. Uniform Tube Model. A Discrete-Time Model Based on Tube Concatenation. Vocal Fold/Vocal Tract Interaction. 5. Analysis and Synthesis of Pole-Zero Speech Models. Time-Dependent Processing. All-Pole Modeling of Deterministic Signals. Linear Prediction Analysis of Stochastic Speech Sounds. Criterion of "Goodness". Synthesis Based on All-Pole Modeling. Pole-Zero Estimation. Decomposition of the Glottal Flow Derivative. Appendix 5.A: Properties of Stochastic Processes. Random Processes. Ensemble Averages. Stationary Random Process. Time Averages. Power Density Spectrum. Appendix 5.B: Derivation of the Lattice Filter in Linear Prediction Analysis. 6. Homomorphic Signal Processing. Concept. Homomorphic Systems for Convolution. Complex Cepstrum of Speech-Like Sequences. Spectral Root Homomorphic Filtering. Short-Time Homomorphic Analysis of Periodic Sequences. Short-Time Speech Analysis. Analysis/Synthesis Structures. Contrasting Linear Prediction and Homomorphic Filtering. 7. Short-Time Fourier Transform Analysis and Synthesis. Short-Time Analysis. Short-Time Synthesis. Short-Time Fourier Transform Magnitude. Signal Estimation from the Modified STFT or STFTM. Time-Scale Modification and Enhancement of Speech. Appendix 7.A: FBS Method with Multiplicative Modification. 8. Filter-Bank Analysis/Synthesis. Revisiting the FBS Method. Phase Vocoder. Phase Coherence in the Phase Vocoder. Constant-Q Analysis/Synthesis. Auditory Modeling. 9. Sinusoidal Analysis/Synthesis. Sinusoidal Speech Model. Estimation of Sinewave Parameters. Synthesis. Source/Filter Phase Model. Additive Deterministic-Stochastic Model. Appendix 9.A: Derivation of the Sinewave Model. Appendix 9.B: Derivation of Optimal Cubic Phase Parameters. 10. Frequency-Domain Pitch Estimation. A Correlation-Based Pitch Estimator. Pitch Estimation Based on a "Comb Filter<170. Pitch Estimation Based on a Harmonic Sinewave Model. Glottal Pulse Onset Estimation. Multi-Band Pitch and Voicing Estimation. 11. Nonlinear Measurement and Modeling Techniques. The STFT and Wavelet Transform Revisited. Bilinear Time-Frequency Distributions. Aeroacoustic Flow in the Vocal Tract. Instantaneous Teager Energy Operator. 12. Speech Coding. Statistical Models of Speech. Scaler Quantization. Vector Quantization (VQ). Frequency-Domain Coding. Model-Based Coding. LPC Residual Coding. 13. Speech Enhancement. Introduction. Preliminaries. Wiener Filtering. Model-Based Processing. Enhancement Based on Auditory Masking. Appendix 13.A: Stochastic-Theoretic parameter Estimation. 14. Speaker Recognition. Introduction. Spectral Features for Speaker Recognition. Speaker Recognition Algorithms. Non-Spectral Features in Speaker Recognition. Signal Enhancement for the Mismatched Condition. Speaker Recognition from Coded Speech. Appendix 14.A: Expectation-Maximization (EM) Estimation. Glossary.Speech Signal Processing.Units.Databases.Index.About the Author.

Discrete-Time Speech Signal Processing: Principles and Practice

Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex.

The influence of signal duration, signal frequency and masker duration on binaural masking level differences.

Sensitivity to AV (auditory-visual) asynchrony was measured using brief tonal signals accompanied by a moving disk on a monitor. Stimuli were presented which either had synchronous audio and video or a positive or negative delay between audio and video. In a detection task, video delays of 29 ms and audio delays of 85 ms were just detectable. In a second experiment subjects had to judge whether the audio and the video were synchronous, whether audio preceded video, or the reverse. Results show a considerable variability in the range over which stimuli were judged to be synchronous. Typically, stimuli with audio delays in the range of -75 to 175 ms were judged synchronous. The distribution of synchronous responses had a mean of around 40 ms. The different results of the first and second experiment indicate that tolerance to AV delays is considerably larger than the just noticeable AV delay. In a next set of experiments several subsequent AV events were presented with variable amounts of jitter in the AV timing. Results of an experimental condition with a constant amount of overall AV delay indicate that the jitter was highly detectable (thresholds as low as 40 ms) even if an overall delay of up to 100 ms was presented. An additional experiment with a variable number of subsequent AV events shows that detecting an asynchrony in one AV event is more difficult than detecting jitter in multiple AV events.

Sensitivity to auditory-visual asynchrony and to jitter in auditory-visual timing

Shannon [J. Acoust. Soc. Am. 91, 2156–2164 (1992)] reports a strong decrease of modulation detection thresholds with cochlear implant patients for an increase of the stimulus level. His conclusion that this result differs from the findings for acoustical stimulation in normal‐hearing subjects needs some clarification. His statement about acoustic stimulation is based on modulation detection measurements for noise carriers, where, indeed, thresholds in normal‐hearing subjects are nearly independent of level, at least for carrier levels of more than 20 dB SL. For sinusoidal carriers, however, the modulation detection thresholds decrease with increasing carrier levels over the entire hearing range, similar to his findings with implant listeners.

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Comment on ``Temporal modulation transfer functions in patients with cochlear implants'' [J. Acoust. Soc. Am. 91, 2156-2164 (1992)]

Temporal resolution and modulation analysis in models of the auditory system

https://pure.tue.nl/ws/files/1547760/622413.pdf

AG Armin Kohlrausch

Papers

The influence of signal duration, signal frequency and masker duration on binaural masking level differences.

Sensitivity to auditory-visual asynchrony and to jitter in auditory-visual timing

Comment on ``Temporal modulation transfer functions in patients with cochlear implants'' [J. Acoust. Soc. Am. 91, 2156-2164 (1992)]

Temporal resolution and modulation analysis in models of the auditory system

Using an excitation-pattern model to predict auditory masking