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.

/pdf/yin-a-fundamental-frequency-estimator-for-speech-and-music-53voam3nec.pdf

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.

Simulationen der Mithörschwellen von Testtönen in dichotischen Rauschmaskierern

Masked thresholds of a 500‐Hz sinusoid were measured in an NρSπ condition for both running and frozen‐noise maskers using a 3IFC procedure. The nominal masker correlation varied between 0.64 and 1, and the bandwidth of the masker was either 10, 100, or 1000 Hz. The running‐noise thresholds were expected to be higher than the frozen‐noise thresholds because of interaural correlation uncertainty of the masker intervals for running‐noise conditions. Since this correlation uncertainty decreases with increasing masker bandwidth, differences between running‐ and frozen‐noise conditions should decrease with increasing bandwidth for interaural correlations smaller than +1. For an interaural correlation close to +1, no difference between frozen‐noise and running‐noise thresholds is expected for all values of the masker bandwidth. These expectations are supported by the experimental data. For the 10‐Hz running‐noise condition, the thresholds can be accounted for in terms of interaural correlation uncertainty. For t...

Measuring the role of masker‐correlation uncertainty in binaural masking experiments

The present invention relates to an object opinion registering device (20) for guiding a person in a decision making situation involving one or more physical objects (10A-10F), the object opinion registering device (20) being arranged to register opinion indications of each of the one or more physical objects (10A-10F), and the object opinion registering device comprising a controller (26) arranged to control at least one object indicator (30) arranged to physically highlight a physical object (10B), among the one or more physical objects (10A-10F), based on registered opinion indications of each of the one or more physical objects (10A-10F).

Object opinion registering device for guiding a person in a decision making situation

Research and teaching in psychoacoustics at German universities

The ability to discriminate two noise auditory stimuli increases with bandwidth. This ability also increases with duration, but only up to a duration of about 25 to 40 ms. Beyond this duration the discriminability decreases. In template-matching and multiple-look models [e.g. Dau et al., J. Acoust. Soc. Am. 99, 3615-3622 (1996)] this decrease of discriminability with increasing duration is not modelled correctly. At most, discriminability remains constant for durations above 25 to 40 ms. This suggests there is a limitation in the information processing capacity for long duration stimuli. The current study proposes a model which restricts the amount of stimulus information across time that is allowed in the internal representation of each noise token to a fixed amount. This approach implies is that stimuli with different durations have incompatible internal representations. Accordingly, the model predicts that the ability to discriminate degrades strongly when a fringe is added to one of the stimuli. A listening test confirmed the model prediction that adding a fringe to one of the stimuli severely impairs the discriminability. Apparently stimulus fringes cannot be ignored, suggesting that these stimuli are processed as inseparable units which is in line with the basic assumptions of the model.

http://www.sea-acustica.es/WEB_ICA_07/fchrs/papers/ppa-07-016.pdf

AG Armin Kohlrausch

Papers

Simulationen der Mithörschwellen von Testtönen in dichotischen Rauschmaskierern

Measuring the role of masker‐correlation uncertainty in binaural masking experiments

Object opinion registering device for guiding a person in a decision making situation

Research and teaching in psychoacoustics at German universities

Processing capacity for perceptual information in Gaussian noise tokens