A system for the automatic transcription of music is described. Signal processing methods are introduced that solve different facets of the overall problem. Main emphasis is laid on finding the multiple pitches of concurrent musical sounds. Sound onset detection and musical meter estimation are described to some extent. Other topics discussed are noise robustness, estimation of the number of co...

Introduction This paper deals relations between randomness and structure in audio and musical sounds. Randomness, in layman sense, would be something that has an element of variation or surprise to it, while structure would be more predictable, rule-based or even deterministic. Dealing with noise, which is the “purest” type of randomness, one usually adopts the canonical physical or engineering...

Musical training strengthens speech perception in the presence of background noise. Given that the ability to make use of speech sound regularities, such as pitch, underlies perceptual acuity in challenging listening environments, we asked whether musicians' enhanced speech-in-noise perception is facilitated by increased neural sensitivity to acoustic regularities. To this aim we examined subco...

The beneficial effects of musical training are not limited to enhancement of musical skills, but extend to language skills. Here, we review evidence that musical training can enhance reading ability. First, we discuss five subskills underlying reading acquisition-phonological awareness, speech-in-noise perception, rhythm perception, auditory working memory, and the ability to learn sound patter...

In the field of speech signal processing, Spectral subtraction method (SSM) has been successfully implemented to suppress the noise that is added acoustically. SSM does reduce the noise at satisfactory level but musical noise is a major drawback of this method. To implement spectral subtraction method, transformation of speech signal from time domain to frequency domain is required. On the othe...

An improved spectral subtraction algorithm for enhancing speech corrupted by additive wideband noise is described. The artifactual noise introduced by spectral subtraction that is perceived as musical noise is 7 dB less than that introduced by the classical spectral subtraction algorithm of Berouti et al. Speech is decomposed into voiced and unvoiced sections. Since voiced speech is primarily s...

Older adults frequently complain that while they can hear a person talking, they cannot understand what is being said; this difficulty is exacerbated by background noise. Peripheral hearing loss cannot fully account for this age-related decline in speech-in-noise ability, as declines in central processing also contribute to this problem. Given that musicians have enhanced speech-in-noise percep...

Recently, I.Cohen has presented causal and noncausal algorithms to modify the classic decision-directed approach for prior SNR. It is well-known that prior SNR is critical to trade off the musical noise level and the audible clearness level in spectral subtraction speech enhancement. However, all these algorithms conflict with statistical signal model more or less. To adjust smoothing parameter...

OBJECTIVE To measure biologic effects of music, noise, and healing energy without human preferences or placebo effects using seed germination as an objective biomarker. METHODS A series of five experiments were performed utilizing okra and zucchini seeds germinated in acoustically shielded, thermally insulated, dark, humid growth chambers. Conditions compared were an untreated control, musica...

When generating musical sound on a digital computer, it is important to have a good model whose parameters provide a rich source of meaningful sound transformations. Three basic model types are in prevalent use today for musical sound generation: instrument models, spectrum models, and abstract models. Instrument models attempt to parametrize a sound at its source, such as a violin, clarinet, o...