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 acoustic environments in hospitals, particularly in intensive care units (ICUs), are characterized by frequent high-level sound events which may negatively affect patient outcome. Many studies performed acoustic surveys, but the measurement protocol was not always reported in detail, and the scope of analysis was limited by the selected mode of sound level meters. Fewer studies systematically investigated the noise sources in ICUs by employing an observer in the patient room, which may potentially bias the measurement. In the current study, the soundscape of an ICU was evaluated where acoustic parameters were extracted from a ∼67-h audio recording, and a selected 24-h recording was annotated off-line for a source-specific analysis. The results showed that the patient-involved noise accounted for 31% of the acoustic energy and 11% of the predicted loudness peaks (PLPs). Excluding the patient-involved noise, the remaining acoustic energy was attributed to staff members (57%), alarms (30%), and the operational noise of life-supporting devices (13%). Furthermore, the contribution of each noise category to the PLPs was found to be more uneven: Staff (92%), alarms (6%), and device noise (2%). The current study suggests that most of the noise sources in ICUs may be associated with modifiable human factors.

Analysis of the soundscape in an intensive care unit based on the annotation of an audio recording.

In our natural environment, we simultaneously receive information through various sensory modalities. The properties of these stimuli are coupled by physical laws, so that, e. g., auditory and visual stimuli caused by the same event have a specific temporal, spatial and contextual relation when reaching the observer. In speech, for example, visible lip movements and audible utterances occur in close synchrony, which contributes to the improvement of speech intelligibility under adverse acoustic conditions. Research into multi-sensory perception is currently being performed in a number of different experimental and application contexts. This chapter provides an overview of the typical research areas dealing with audio—visual interaction3 and integration, bridging the range from cognitive psychology to applied research for multi-media applications. A major part of this chapter deals with a variety of research questions related to the temporal relation between audio and video. Other issues of interest are basic spatio-temporal interaction, spatio-temporal effects in audio—visual stimuli — including the ventriloquist effect, cross-modal effects in attention, audio—visual interaction in speech perception and interaction effects with respect to the perceived quality of audio—visual scenes.

Audio—Visual Interaction in the Context of Multi-Media Applications

The shape of the auditory filter was calculated from binaural masking experiments. Two different types of maskers were used in the study, a masker that was interaurally in phase at all frequencies (No), and a masker with an interaural phase difference of 0 below 500 Hz and of π above 500 Hz. The test‐signal frequency varied between 200 and 800 Hz, and the test signal was presented either monaurally (Sm) or binaurally in antiphase (Sπ). By comparing the masked thresholds from the two experimental conditions, the following conclusion can be drawn: The threshold of the test signal is only affected by the masker phase within a narrow frequency range around the test frequency. Thus, for test‐signal frequencies well above or below 500 Hz, no influence of the phase transition on the BMLD is observed, and normal masked thresholds for No and Nπ maskers are obtained. For test frequencies around 500 Hz, the step in interaural phase difference leads to a decrease in the interaural correlation of the masker within the critical band around the test‐signal frequency. This results in strong threshold changes for both monaural and binaural signals. A calculation of the auditory filter shape from the masked threshold values was performed under the assumption that the masked threshold is only dependent on the interaural cross correlation of the masker within the filter band. Using the formula of the EC theory for the relation between masker correlation and BMLD, the experimental data are well described by a trapezoidal filter with an equivalent rectangular bandwidth of 80 to 84 Hz.

Auditory filter shape derived from binaural masking experiments.

Perception of the size and speed of rolling balls by sound

A complex tone often evokes a pitch sensation associated with its extreme spectral components, besides the holistic pitch associated with its fundamental frequency. We studied the edge pitch created at the upper spectral edge of complexes with a low-pass spectrum by asking subjects to adjust the frequency of a sinusoidal comparison tone to the perceived pitch. Measurements were performed for different values of the fundamental frequency and of the upper frequency of the complex as well as for three different phase relations of the harmonic components. For a wide range of these parameters the subjects could adjust the comparison tone with a high accuracy, measured as the standard deviation of repeated adjustments, to a frequency close to the nominal edge frequency. The detailed dependence of the matching accuracy on temporal parameters of the harmonic complexes suggests that the perception of the edge pitch in harmonic signals is related to the temporal resolution of the hearing system. This resolution depends primarily on the time constants of basilar-membrane filters and on additional limitations due to neuronal processes.

AG Armin Kohlrausch

Papers

Analysis of the soundscape in an intensive care unit based on the annotation of an audio recording.

Audio—Visual Interaction in the Context of Multi-Media Applications

Auditory filter shape derived from binaural masking experiments.

Perception of the size and speed of rolling balls by sound

Pitch related to spectral edges of broadband signals.