Search results for "Music Information Retrieval"
showing 10 items of 21 documents
Audio based genre classification of electronic music
2007
Predicting Music Therapy Clients’ Type of Mental Disorder Using Computational Feature Extraction and Statistical Modelling Techniques
2009
Background. Previous work has shown that improvisations produced by clients during clinical music therapy sessions are amenable to computational analysis. For example, it has been shown that the perception of emotion in such improvisations is related to certain musical features, such as note density, tonal clarity, and note velocity. Other work has identified relationships between an individual’s level of mental retardation and features such as amount of silence, integration of tempo with the therapist, and amount of dissonance. The present study further develops this work by attempting to predict music therapy clients’ type of mental disorder, as clinically diagnosed, from their improvisat…
The influence of rhythmic and spectro-timbral musical features on gait-related movement
2017
Music makes us move, and humans have the universal tendency to synchronise their movements to music. This phenomenon has been used in music therapy to help people with movement disorders regain control over their movements. Rhythmic auditory stimulation has shown promising results in gait rehabilitation in various clinical populations. In healthy populations, various differences have been found between movement while walking to musical and metronome stimuli in terms of stride length. However, insufficient research has been conducted concerning the musical features that could evoke this difference, and which gait-related movements might change under the influence of music. The aim of this mo…
The Pursuit of Happiness in Music: Retrieving Valence with Contextual Music Descriptors
2009
In the study of music emotions, Valence is usually referred to as one of the dimensions of the circumplex model of emotions that describes music appraisal of happiness, whose scale goes from sad to happy. Nevertheless, related literature shows that Valence is known as being particularly difficult to be predicted by a computational model. As Valence is a contextual music feature, it is assumed here that its prediction should also require contextual music descriptors in its predicting model. This work describes the usage of eight contextual (also known as higher-level) descriptors, previously developed by us, to calculate happiness in music. Each of these descriptors was independently tested …
On Analytical vs . Schizophrenic Procedures for Computing Music
2009
The authors present a perspective on computer music, which is based on some particular definitions of music in relation to oral culture and cybernetics. They describe some experiments with different models of neural architectures which generate original music, and then suggest that if such neural systems are rich, effective and intuitive enough to produce ‘live’ music, the understanding of their behaviour may require the development of some ‘schizophrenic’ procedures, as well as analytical ones.
Looking Beyond Genres: Identifying Meaningful Semantic Layers from Tags in Online Music Collections
2011
A scheme for identifying the semantic layers of music-related tags is presented. Arguments are provided why the applications of the tags cannot be effectively pursued without a reasonable understanding of their semantic qualities. The identification scheme consists of a set of filters. The first is related with social consensus, user-count ratio, and n-gram properties of tags. The next relies on look-up functions across multiple databases to determine the probable semantic layer of each tag. Examples of the semantic layers with prevalence rates are given based on application of the scheme to a subset of the Million Song Dataset. Finally, a validation of the results was carried out with an i…
Embodied Meter Revisited : Entrainment, Musical Content, and Genre in Music-Induced Movement
2022
Previous research has shown that humans tend to embody musical meter at multiple beat levels during spontaneous dance. This work that been based on identifying typical periodic movement patterns, or eigenmovements, and has relied on time-domain analyses. The current study: 1) presents a novel method of using time-frequency analysis in conjunction with group-level tensor decomposition; 2) compares its results to time-domain analysis, and 3) investigates how the amplitude of eigenmovements depends on musical content and genre. Data comprised three-dimensional motion capture of 72 participants’ spontaneous dance movements to 16 stimuli including eight different genres. Each trial was subjected…
Testing a spectral-based feature set for audio genre classification
2011
Automatic musical genre classification is an important information retrieval task since it can be applied for practical purposes such as the organization of data collections in the digital music industry. However, this task remains an open question because the current state of the art shows far from satisfactory outcomes in terms of classification performance. Moreover, the most common algorithms that are used for this task are not designed for modelling music perception. This study suggests a framework for testing different musical features for use in music genre classification and evaluates the performance of this task based on two musical descriptors. The focus of this study is on automa…
Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening
2019
AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method t…