Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Music emotion detection using hierarchical sparse kernel machines.

Yu-Hao Chin1, Chang-Hong Lin1, Ernestasia Siahaan1

  • 1Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan.

Thescientificworldjournal
|April 15, 2014
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving.

Sensors (Basel, Switzerland)·2021
Same author

Schistosoma egg antigens suppress LPS-induced inflammation in human IMR-90 cells by modulation of JAK/STAT1 signaling.

Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi·2020
Same author

Emotion identification using extremely low frequency components of speech feature contours.

TheScientificWorldJournal·2014
See all related articles

This study introduces a novel music emotion verification system using hierarchical sparse kernel machines to detect happiness in music. The system effectively identifies happy music clips with high accuracy.

Area of Science:

  • Computer Science
  • Music Information Retrieval
  • Machine Learning

Background:

  • Music emotion detection is a challenging task in Music Information Retrieval.
  • Accurate classification of emotions in music is crucial for various applications.
  • Existing methods often struggle with nuanced emotional expression in music.

Purpose of the Study:

  • To develop and evaluate a novel music emotion verification system.
  • To specifically detect the presence or absence of happiness emotion in music clips.
  • To leverage hierarchical sparse kernel machines for improved emotion detection performance.

Main Methods:

  • A two-level hierarchical sparse kernel machine architecture was employed.
  • The first level involved feature extraction, dimensionality reduction using Principal Component Analysis (PCA), and Support Vector Machines (SVMs) with probability product kernels.

Related Experiment Videos

  • The second level utilized a Relevance Vector Machine (RVM) with a radial basis function kernel for happiness verification, using a happiness threshold based on probability values.

    • Main Results:

    • The proposed system demonstrated good performance in verifying happiness emotion in music clips.
    • Detection Error Tradeoff (DET) curves indicated the system's effectiveness.
    • The hierarchical approach successfully integrated acoustic features and machine learning models for emotion classification.

    Conclusions:

    • The hierarchical sparse kernel machine system offers a robust approach for music emotion verification, particularly for detecting happiness.
    • The combination of PCA, SVMs, and RVMs in a hierarchical structure enhances detection accuracy.
    • This system provides a valuable tool for music information retrieval and emotion analysis.