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

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Joris, P. X., C. E. Schreiner, and A. Rees. Neural Processing of Amplitude-Modulated Sounds. Physiol Rev 84: 541–577, 2004; 10.1152/physrev.00029.2003.—Amplitude modulation (AM) is a temporal featu...

https://www.physiology.org/doi/pdf/10.1152/physrev.00029.2003

Neural processing of amplitude-modulated sounds.

The effects of music training in relation to brain plasticity have caused excitement, evident from the popularity of books on this topic among scientists and the general public. Neuroscience research has shown that music training leads to changes throughout the auditory system that prime musicians for listening challenges beyond music processing. This effect of music training suggests that, akin to physical exercise and its impact on body fitness, music is a resource that tones the brain for auditory fitness. Therefore, the role of music in shaping individual development deserves consideration.

/pdf/music-training-for-the-development-of-auditory-skills-4iumvd2s4d.pdf

Music training for the development of auditory skills

The yield of alkenols and cycloalkenols is substantially improved by carrying out the reaction of olefins with formaldehyde in the presence of selected catalysts. In accordance with one embodiment, alk-3-en-1-ols are produced in good yields from isobutylene and formaldehyde in the presence of organic carboxylic acid salts of Group IB metals.

The Organization of the Cochlear Receptor

Noise with a sharp spectral edge, occurring at an edge frequency fe, leads to a tonal sensation with a timbre like that of a sine tone and a pitch near the edge frequency. For lowpass noise the pitch is shifted below fe and for high pass noise the pitch is shifted above. Typical shifts are less than 10% of the edge frequency. To probe this effect, pitch matching experiments were done using 32 different lowpass and highpass noises with edges at 500 and 600 Hz and with durations of about 50 ms. Pitch matches were made in an indefinitely repeating cycle of noises and half-second matching tones. After each match, listeners rated the difficulty of the match. The ratings were ultimately reduced to a five-point scale. The experimental data were compared with the predictions of a model based on the peaks of an all-order population interval distribution as determined by the Zilany-Bruce-Carney model of the auditory periphery. Matching frequencies were predicted from the periodicities of the best fitting subharmoni...

Probing monaural edge pitch

Musical pitches can be perceived in broadband noise stimuli near frequencies corresponding to parameter changes along the frequency dimension. For example, monaural edge pitches (MEPs) are produced by noise stimuli with sharp spectral edges. Binaural edge pitches (BEPs) are produced by dichotic noise with interaural-phase changes at frequency boundaries (e.g., 0-pi), and binaural-coherence edge pitches (BICEPs) arise from boundaries between frequency regions with different interaural coherence (such as changes from correlated to uncorrelated noises). Perceived pitches are shifted slightly in frequency away from spectral edges: MEPs are shifted into noise bands, BICEPs are shifted into the incoherent region, and BEPs are shifted bimodally. This presentation proposes a temporal model for these edge pitches based on population-wide all-order interspike interval distributions (summary autocorrelations, SACFs), computed using the Zilany-Bruce-Carney 2014 model of the auditory nerve. Pitches were estimated from...

Temporal model of edge pitch effects

While I agree with most of the thrust of second-order cybernetics, I find the dichotomy of firstvs. second-order cybernetics conceptually and historically problematic because it implicitly …

https://constructivist.info/11/3/472.cariani.pdf

Beware False Dichotomies

terns. As objects of perception and cognition, musical forms range widely in complexity, from the simplest rhythms, melodies, and harmonies, to some of the most elaborate patterns that can be comprehended by human beings. As an art, the ultimate ends of music are related to expression and evocation of emotion. Both the perceptual-cognitive and aesthetic-hedonic aspects of music must have their correlates in neural informational processes embedded in biological brains. Musical form exists both in time and of time. Successions of sonic events unfold over time to create temporal patterns and expectancies. Music plays on the perceptual and cognitive salience of temporal patterns. Anticipations are formed and violated through repetition and change to create cycles of tension and resolution. Music also exists of time. Basic primitive qualities of pitch, timbre, and rhythm may themselves be represented in the brain as temporal patterns. The neural mechanisms for apprehending harmonic relations in tonal music may therefore be the consequences of the basic neural codes and computations that the brain uses to analyze the auditory environment. Thus not only can there be representations of sequences and patterns of events in time, but also that musical objects themselves may be fundamentally temporal in nature. This issue explores modeling of music and time from different points of view. Peter Cariani, in “Temporal Codes, Timing Nets, and Music Perception”, introduces the notion of timing nets as neural structures for temporal information processing on neural spike trains. Feedforward timing nets are shown to be able to extract common periodicities underlying pitch and timbre, while recurrent nets are used to build up and extract multiple rhythmic subpatterns. In his “Real-time Recognition of Improvisations with Adaptive Oscillators and a Recursive Bayesian Classifier” Petri Toiviainen gives an overview of his work in tempo tracking and beat induction, which is then applied to a realtime MIDI interactive music system for the recognition and accompaniment of tonal improvisations. William Sethares and Thomas Staley, in “Meter and Periodicity in Musical Performance”, present the periodicity transform as a mathematical tool to locate periodicities in musical audio signals that projects temporal event patterns onto a set of (non-orthogonal) periodic subspaces. The method detects periodicities that correspond to the pulse, the measure, and phrases. Masataka Goto, in “An Audio-Based Real-Time Beat Tracking System for Music with or without Drum-sounds and its Applications”, describes a real-time beat tracking system that recognizes a hierarchical beat structure of quarter-note, half-note, and measure level in real-world audio signals. The model uses frequency analysis based on topdown information, as well as knowledge concerning the presence or absence of drum-sounds. Edward Large, in “Periodicity, Pattern Formation, and Rhythmic Structure”, describes an approach to metrical structure using a self-organized, dynamic structure composed of self-sustaining oscillators. The structure anticipates futures events and attempts to capture the perception of metrical structure through formation of stable patterns of interaction that implement dynamic attentional strategies. Douglas Eck, in “A Positive-Evidence Model for Classifying Rhythmical Patterns”, presents a model that predicts the beat and pattern complexity in rhythmical patterns. Several datasets are used to examine model predictions and to compare them with existing models. In this issue we have attempted to bring together these different aspects of music and time. The idea for this special issue on musical periodicity and rhythm perception came as a result of a workshop on Music and Timing Networks held on 29 October 1999 at the Institute for Psychoacoustics and Electronic Music (IPEM) of Ghent University. The editors wish to thank the Research Society for the Foundations of Music Research of the Belgian Fund for Scientific Research for their generous support of this workshop, which made this issue possible.

Introduction: Music and time

The pitch of complex tones plays an important role not only in speech and music, but also for perceptual segregation of sound sources. In order to study physiological correlates of these phenomena, autocorrelation histograms were recorded and computed from auditory‐nerve fibers in anesthetized cats to estimate the aggregate interspike interval distribution for the entire auditory nerve. Results for many types of complex stimuli (including those used in classical pitch shift and pitch dominance experiments) suggest that the perceived pitch corresponds to the most frequent interspike interval in the auditory nerve. Also investigated was how interspike interval information might be used to separate simultaneously presented vowels. Psychophysical experiments show that both constituents in a pair of concurrent vowels are better identified when they have slight different pitches. These results show that concurrent vowels can usually be segregated from the information in the aggregate interspike interval distrib...

Peter Cariani

Papers

Probing monaural edge pitch

Temporal model of edge pitch effects

Beware False Dichotomies

Introduction: Music and time

Neurophysiological correlates of the pitch of complex tones