Download Music Emotion Classification: Dataset Acquisition And Comparative Analysis
In this paper we present an approach to emotion classification in audio music. The process is conducted with a dataset of 903 clips and mood labels, collected from Allmusic1 database, organized in five clusters similar to the dataset used in the MIREX2 Mood Classification Task. Three different audio frameworks – Marsyas, MIR Toolbox and Psysound, were used to extract several features. These audio features and annotations are used with supervised learning techniques to train and test various classifiers based on support vector machines. To access the importance of each feature several different combinations of features, obtained with feature selection algorithms or manually selected were tested. The performance of the solution was measured with 20 repetitions of 10-fold cross validation, achieving a F-measure of 47.2% with precision of 46.8% and recall of 47.6%.
Download The development of an online course in DSP eartraining
The authors present a collaborative effort on establishing an online course in DSP eartraining. The paper reports from a preliminary workshop that covered a large range of topics such as eartraining in music education, terminology for sound characterization, e-learning, automated tutoring, DSP techniques, music examples and audio programming. An initial design of the web application is presented as a rich content database with flexible views to allow customized online presentations. Technical risks have already been mitigated through prototyping.
Download Impact Of Personalized Equalization Curves On Music Quality In Dichotic Listening
This paper investigated the impact of personalized equalization (EQ) on music quality. A pair of personalized EQ curves was found for each listener in a double-reference psychoacoustic test. Original high-fidelity music and music equalized by the pair of personalized EQ curves were randomly presented to listeners who were instructed to rate music quality. Statistical analysis showed that personally equalized music provided significantly higher music quality than original music.
Download The Helmholtz Resonator Tree
The Helmholtz resonator is a prototype of a single acoustic resonance, which can be modeled with a digital resonator. This paper extends this concept by coupling several Helmholtz resonators. The resulting structure is called a Helmholtz resonator tree. The height of the tree is defined by the number of resonator layers that are interconnected. The overall number of resonance frequencies of a Helmholtz resonator tree is the same as its height. A Helmholtz resonator tree can be modeled using wave digital filters (WDF), when electro-acoustic analogies are applied. A WDF tool for implementing Helmholtz resonator trees has been developed in C++. A VST plugin and an Android mobile application were created, which can run short Helmholtz resonator trees in real time. Helmholtz resonator trees can be used for the real-time synthesis of percussive sounds and for realizing novel filtering which can be tuned using intuitive physical parameters.