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In the modern educational process music adapts to changes of everyday life and develops in the context of modern technologies. Following the needs, interests and abilities of the preschooler, preschool teachers use music through all available means of sound reproduction, combining it with their own potential and capabilities of musical expression. This paper is the result of the growing intellectual and musical potential of a preschooler who actively engages different senses in learning music: from sound to colour. The aim is to create a computer application which facilitates music learning through learning colours. The method is determined by using the application within the Max / MSP / Jitter (Max) programming language. Max is a visual programming language whose environment is based on combining objects and applications into configurations intended for music and multimedia. The aim of the paper is overcoming the basic concepts of musical literacy through using a specific colour as a carrier of organizing the music flow. The potential of this program aimed at preschool children is that learning music in the modern educational system is understood through a compromise with the development of the demands of modern technology of which music is an integral part.

Electronic music emotion recognition technology uses computers to analyze and process music features, study the mapping relationship between music feature space and emotional space, and realize the cognitive process of the emotions expressed by music. Based on the BP neural network, this paper analyzes the emotion recognition algorithms of different electronic music signals. First, according to the organizational structure of music, a musical model was established. Then, use timbre to divide the music into a number of independent sections. Finally, the BP network algorithm is used to identify electronic music. Experiments show the effectiveness of the algorithm.

Musical training causes structural and functional changes in the brain due to its sensory-motor demands. This leads to differences in how musicians perceive and process music as compared to non-musicians, thereby providing insights into brain adaptations and plasticity. Correlational studies and network analysis investigations have indicated the presence of large-scale brain networks involved in the processing of music and have highlighted differences between musicians and non-musicians. However, studies on functional connectivity in the brain during music listening tasks have thus far focused solely on static network analysis. Dynamic Functional Connectivity (DFC) studies have lately been found useful in unearthing meaningful, time-varying functional connectivity information in both resting-state and task-based experimental settings. In this study, we examine DFC in the fMRI obtained from two groups of participants, 18 musicians and 18 non-musicians, while they listened to a musical stimulus in a naturalistic setting. We utilize spatial Group Independent Component Analysis (ICA), sliding time window correlations, and a deterministic agglomerative clustering of windowed correlation matrices to identify quasi-stable Functional Connectivity (FC) states in the two groups. To compute cluster centroids that represent FC states, we devise and present a method that primarily utilizes windowed correlation matrices occurring repeatedly over time and across participants, while excluding matrices corresponding to spontaneous fluctuations. Preliminary analysis indicate states with greater visuo-sensorimotor integration in musicians, larger presence of DMN states in non-musicians, and variability in states found in musicians due to differences in training and prior experiences. peerReviewed

Data mining tasks such as music indexing, information retrieval, and similarity search, require an understanding of how listeners process music internally. Many algorithms for automatically analyzing the structure of recorded music assume that a large change in one or another musical feature suggests a section boundary. However, this assumption has not been tested: while our understanding of how listeners segment melodies has advanced greatly in the past decades, little is known about how this process works with more complex, full-textured pieces of music, or how stable this process is across genres. Knowing how these factors affect how boundaries are perceived will help researchers to judge the viability of algorithmic approaches with different corpora of music. We present a statistical analysis of a large corpus of recordings whose formal structure was annotated by expert listeners. We find that the acoustic properties of boundaries in these recordings corroborate findings of previous perceptual experiments. Nearly all boundaries correspond to peaks in novelty functions, which measure the rate of change of a musical feature at a particular time scale. Moreover, most of these boundaries match peaks in novelty for several features at several time scales. We observe that the boundary-novelty relationship can vary with listener, time scale, genre, and musical feature. Finally, we show that a boundary profile derived from a collection of novelty functions correlates with the estimated salience of boundaries indicated by listeners.

Sensitivity for music is a diffuse property of the human brain. When we listen to music, the entire brain is active. In particular, the auditory cortex has connections to the frontal lobe of the brain where many of our capabilities for abstraction and inference are located. A widely accepted idea (although it is unproven) is that humans have an inborn capacity to process music. David Huron highlighted the adaptive advantage of having a mind able to recognize sound patterns and to extract complex significances from a heterogeneous flux of sounds arriving from the environment (Huron, 1991). Probably our ability to recognize complex sound patterns is one of the results of natural evolution of humans' cognitive capabilities (Ball, 2010). These concepts suggest the following idea: we could use complex sounds (and music in particular), and not only images, for geophysical data analysis, interpretation, and integration. An advantage to this could derive from the fact that sounds have many physical, perceptive, psychological, and cultural “dimensions”: this implies an expanded range of possibilities for analyzing and interpreting geophysical signals transformed into sounds. […] Human sensitivity for music (and for sounds in general) is nowadays supported by advanced technology and by many formats and informatics' protocols used in digital music (such as wav, mp3, MIDI). These allow easy sound manipulation, analysis, and integration. In particular, the MIDI protocol links heterogeneous information through multiple types of media. There is an additional reason why geophysical data analysis/interpretation could be performed in the domain of sounds and music: the nature of several types of geophysical signals, like seismic data, is closer to the physics of sounds than to the physics of images. These ideas have been investigated (partially) by other authors (Benioff, 1953; Speeth, 1961; Hayward, 1994; Kilb et al., 2012; Peng et al., 2012). In this article, I expand them by proposing a new approach based on exploring new interesting connections between geosciences and the most recent technological innovations in the domain of digital music.

Music structural analysis tasks have an important position in the field of Music information retrieval which require an understanding of how humans process music internally, such as music indexing, music summarization, and similarity analysis. Many schemes have been proposed to analyze the structure of recorded music, however they usually use single feature to detect boundaries of songs and the results are not satisfactory. In this paper, we present a method which is based on novelty detection and combines multiple features to the task of music boundaries detection. We extract peaks of novelty function derived from various features as potential boundaries, then eliminate non-boundaries from potential boundaries derived from distinct feature sets. Three types of features, including intensity, timbre, and harmony are employed to represent the characteristics of a music clip. On our testing database composed of 175 entire songs, the best accuracy of boundary detection with tolerance ±3 seconds achieves up to 65.7%.

Query by Singing/Humming (QBSH) is a most natural way for music search. A music search system can help music finders search songs by matching a part of melody by singing or humming. Many music information retrieval techniques have been developed to carry out music search for years. On the other hand, thanks to the rapid growth of mobile wireless Internet technologies this decade, music search applications can be implemented on hand-carried devices, such as cellular phones, to conduct music search anytime and anywhere via any available networks, such as Wi-Fi, UMTS, WiMAX to the emerging 3GPP-LTE networks. In the past, little studies had ever been revealed about how to design and implement a lightweight music search engine over a fixed or mobile Internet. In this article, we aim to elaborate a practical skeleton of developing a simple music search engine over fixed or mobile networks--a Fixed-Mobile Convergent Music Search Engine (FMC-MUSE). FMC-MUSE can process music queries by QBSH from fixed or mobile clients and return a dataset containing the search results and meta-info back to music finders via ubiquitous networks.

As an important way of music retrieval, Query-By-Humming has gained wide attention because of its effectiveness and convenience. This paper proposes a novel Top-K similarity search technique, which provides fast retrieval for Query-By-Humming. We propose a distance function MDTW for multi-dimensional sequence matching as well as a subsequence matching method \(MDTW_{sub}\). We show that the proposed method is highly applicable to music retrieval. In our paper, music pieces are represented by 2-dimensional time series, where each dimension holds information about the pitch or duration of each note, respectively. In order to improve the efficiency, we utilize inverted lists and q-gram technique to process music database, and utilize q-chunk technique to process hummed piece. Then, we calculate the MDTW distances between hummed q-chunks and music q-grams, and we can get the candidate music and their sensitive data areas. We proposes TopK-Brute and TopK-LB Algorithm to search the Top-K songs. The experimental results demonstrate both the efficiency and effectiveness of our approach.

The next step for the paradigm of embodied music cognition (EMC) is to move from building theories of (empirical) data toward building systematic explanations of organisms' capacities to process music. I argue that for the mirror neuron (MN) hypothesis to be effective when applied to music, these neurons' capacities should be proven to be nontrivially and causally connected with musical organisms as a whole. This requires, I argue, a mechanistic type of explanation.

This paper describes a real-time beat tracking system that recognizes a hierarchical beat structure comprising the quarter-note, half-note, and measure levels in real-world audio signals sampled from popular-music compact discs. Most previous beat-tracking systems dealt with MIDI signals and had difficulty in processing, in real time, audio signals containing sounds of various instruments and in tracking beats above the quarter-note level. The system described here can process music with drums and music without drums and can recognize the hierarchical beat structure by using three kinds of musical knowledge: of onset times, of chord changes, and of drum patterns. This paper also describes several applications of beat tracking, such as beat-driven real-time computer graphics and lighting control.

Existing music search engines process music as they would process text, ignoring the richness of this media. We introduce here MuMa1 (MUsic MAshup), a web application which makes the most of audio contents (the signal itself) to provide the user with new ways of browsing music. MuMa allows the user to search for particular chords sequences, to generate smart playlists and to extend its musical universe.

Existing music search engines process music as they would process text, ignoring the richness of this media. We introduce here MuMa1 (MUsic MAshup), a web application which makes the most of audio contents (the signal itself) to provide the user with new ways of browsing music. MuMa allows the user to search for particular chords sequences, to generate smart playlists and to extend its musical universe.

This paper introduces a software architecture for the representation and real-time processing of sound and music, based on Artificial Intelligence techniques. In particular, a system able to store and process music and sounds at different levels of abstraction, and to carry out plans for manipulating this material according to the user's goals is described. This system is able to generate new music pieces as well as manipulate existent ones, on the basis of given material: to this end, it provides also formal analysis capabilities on both music and sounds for extracting information useful in subsequent synthesis processes.

Simulations of self-organizing neuronlike networks are used to demonstrate how untrained listeners might be able to sort their perceptions of dozens of diverse musical features into stable, meaningful schemata. A presentation is first made of the salient characteristics of such networks, especially the adaptive-resonance-theory (ART) networks proposed by Stephen Grossberg. Then a discussion follows of how a computer simulation of a four-level ART network - a simulation dubbed L'ART pour l'art - independently categorized musical events in Mozart's six earliest compositions. The ability of the network to abstract significant voiceleading combinations from these pieces (and in fact to detect a possible error in the New Mozart Edition) suggests that this approach holds promise for the study of how ordinary listeners process music's multidimensional complexity. In addition, the categorizations produced by the network are suggestive of alternative conceptualizations of music's hierarchical structure. a prim household, tidiness is a paramount virtue. Dishes line cupboards like soldiers on parade. Books march across shelves in close-order drill. There is a place for everything, and everything is in its place. A visitor senses

The focus in hearing aids is generally on speech, but the ability to reproduce music is also important for listener satisfaction. This study uses a sound quality metric to predict judgments of music quality made by normal‐hearing and hearing‐impaired listeners. A simulated hearing aid was used to process music modified by (1) additive noise and nonlinear processing, (2) linear filtering, and (3) combinations of noise, nonlinear processing, and linear filtering. The music stimuli included jazz, classical, and vocalize selections. The quality metric incorporates a model of the auditory periphery that reproduces aspects of impaired hearing. The effects of noise and distortion on the signal are estimated by measuring the changes to the envelope time‐frequency modulation pattern, and the effects of linear filtering are estimated by measuring the changes to the signal long‐term internal spectral representation. The accuracy of the quality metric for music will be presented, and the performance of the metric for music will be compared to its accuracy in reproducing the quality ratings of speech stimuli processed under similar conditions.

This paper is a brief overview of the research and didactic activities of the Laboratorio di Informatica Musicale (L.I.M.) of the University of Milan. The main goals at L.I.M. are the definition of formal methods and the implementation of software and hardware tools to process music (both in real time and in interactive mode) at various abstraction levels.

To process music might appear easy, but to exchange musical information, in real time, between a computer and a device seems more difficult. Our paper presents a realization of a musical peripheral built with an electronic organ and that allows a computer to get the information that comes from the keyboard or play music on the organ.The system is not too complex because our first preocupation was to encode music under a digital form, and then to create automatically the encoding. We present algorithms, realizations and perspectives.