Modulation Spectral Features
In Pursuit of Invariant Representations of Music with Application to Unsupervised Source Identification
Bibliographic Data
| ID | 7857571 |
|---|---|
| Authors | Nashlie H Sephus (Georgia Institute of Technology, corresponding author), Aaron D Lanterman (0000-0002-4618-7988, Georgia Institute of Technology), David V Anderson (0000-0002-8571-4613, Georgia Institute of Technology) |
| Year | 2015 |
| Volume | 44 |
| Issue | 1 |
| Pages | 58-70 |
| Publication date | 2015-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of New Music Research (JOURNAL) |
| Journal identifiers | ISSN: 0929-8215 • E-ISSN: 1744-5027 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09298215.2014.916723 |
| OpenAlex | W2100786247 |
| Language | EN |
| Citations received | 1 |
| References cited | 60 |
Modulation frequency analysis has been studied predominantly in research areas such as communications, filtering and coding of digital signals, and representations of neurons in the biomedical field. In turn, modulation frequency features have surfaced in the area of music data mining, also known as music information retrieval (MIR). The term ‘modulation spectral features’ has been used rather loosely, but we refer to them as temporal patterns in a signal that may be revealed via the modulation spectrum. This paper consists of a literature survey of modulation features in which we review the historical use of general modulation features along with evolving interest in exploring modulation spectral features for data mining in music signals. We also discuss challenges encountered in employing modulation spectral features with music signals and suggest new directions in this area. Lastly, we further exploit modulation spectral features by applying these motivated concepts, properties, and parameters of modulation spectra and its features to our preliminary, unsupervised source identification method for sound sources of periodic, temporal patterns
Acoustics · Frequency modulation · Physics · Radio frequency · Speech recognition · Telecommunications · Animal Vocal Communication and Behavior · Computer Science · Mathematics · Music and Audio Processing · Speech and Audio Processing · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.17 |
| Citation span | 2020 - 2020 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |