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Denny Iskandar

dblp:82/444 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Audio and music processing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › music information retrieval › music alignment
lyrics-to-audio alignment
0.122008
LyricAlly: Automatic Synchronization of Textual Lyrics to Acoustic Music Signals · IEEE Trans. Speech Audio Process. 2008
Syllabic level automatic synchronization of music signals and text lyrics · ACM Multimedia 2006
Audio and music processing › music information retrieval › singing information processing
singing voice detection
0.112008
LyricAlly: Automatic Synchronization of Textual Lyrics to Acoustic Music Signals · IEEE Trans. Speech Audio Process. 2008
Audio and music processing › music structure analysis
chorus detection
0.012008
LyricAlly: Automatic Synchronization of Textual Lyrics to Acoustic Music Signals · IEEE Trans. Speech Audio Process. 2008

Methods — techniques the papers use, named apart from their topics

text processing · 0.1hierarchical rhythm structure analysis · 0.1audio feature extraction · 0.1syllabic level synchronization · 0.1
YearPublicationVenuePosition
2008 LyricAlly: Automatic Synchronization of Textual Lyrics to Acoustic Music Signals
abstract
We present LyricAlly, a prototype that automatically aligns acoustic musical signals with their corresponding textual lyrics, in a manner similar to manually-aligned karaoke. We tackle this problem based on a multimodal approach, using an appropriate pairing of audio and text processing to create the resulting prototype. LyricAlly's acoustic signal processing uses standard audio features but constrained and informed by the musical nature of the signal. The resulting detected hierarchical rhythm structure is utilized in singing voice detection and chorus detection to produce results of higher accuracy and lower computational costs than their respective baselines. Text processing is employed to approximate the length of the sung passages from the lyrics. Results show an average error of less than one bar for per-line alignment of the lyrics on a test bed of 20 songs (sampled from CD audio and carefully selected for variety). We perform a comprehensive set of system-wide and per-component tests and discuss their results. We conclude by outlining steps for further development.
Min-Yen Kan, Ye Wang 0007, Denny Iskandar, Tin Lay Nwe, Arun Shenoy
IEEE Trans. Speech Audio Process.3
2006 Syllabic level automatic synchronization of music signals and text lyrics
abstract
10.1145/1180639.1180777
Denny Iskandar, Ye Wang 0007, Min-Yen Kan, Haizhou Li 0001
ACM Multimedia1