There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.

WaveNet: A Generative Model for Raw Audio

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An Introduction To The Event Related Potential Technique

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

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

https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/Speak08To09/STRAIGHT-original.pdf

Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds

This chapter discusses models of pitch, old and recent. The aim is to chart their common points – many are variations on a theme – and differences, and build a catalog of ideas for use in understanding pitch perception. The busy reader might read just the next section, a crash course in pitch theory that explains why some obvious ideas don’t work and what are currently the best answers. The brave reader will read on as we delve more deeply into the origin of concepts, and the intricate and ingenious ideas behind the models and metaphors that we use to make progress in understanding pitch.

/pdf/pitch-perception-models-26s9yzufwh.pdf

Pitch Perception Models

http://audition.ens.fr/adc/pdf/2007_JNeurosciMethods_TSPCA.pdf

Denoising based on time-shift PCA

An accurate fundamental frequency (F0) estimation method for non-stationary, speech-like sounds is proposed based on the differential properties of the instantaneous frequencies of two sets of filter outputs. A specific type of fixed points of mapping from the filter center frequency to the output instantaneous frequency provides frequencies of the constituent sinusoidal components of the input signal. When the filter is made from an isometric Gabor function convoluted with a cardinal B-spline basis function, the differential properties at the fixed points provide practical estimates of the carrier-to-noise ratio of the corresponding components. These estimates are used to select the fundamental component and to integrate the F0 information distributed among the other harmonic components.

Alain de Cheveigné

Papers

YIN, a fundamental frequency estimator for speech and music

Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds

Pitch Perception Models

Denoising based on time-shift PCA

Fixed point analysis of frequency to instantaneous frequency mapping for accurate estimation of F0 and periodicity