End-to-End Optimization of Source Models for Speech and Audio Coding Using a Machine Learning Framework

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

3 Sitaatiot (Scopus)
451 Lataukset (Pure)

Abstrakti

Speech coding is the most commonly used application of speech processing. Accumulated layers of improvements have however made codecs so complex that optimization of individual modules becomes increasingly difficult. This work introduces machine learning methodology to speech and audio coding, such that we can optimize quality in terms of overall entropy. We can then use conventional quantization, coding and perceptual models without modification such that the codec adheres to conventional requirements on algorithmic complexity, latency and robustness to packet loss. Experiments demonstrate that end-to-end optimization of quantization accuracy of the spectral envelope can be used for a lossless reduction in bitrate of 0.4 kbits/s.
AlkuperäiskieliEnglanti
OtsikkoProceedings of Interspeech
KustantajaInternational Speech Communication Association (ISCA)
Sivut3401-3405
DOI - pysyväislinkit
TilaJulkaistu - syysk. 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInterspeech - Graz, Itävalta
Kesto: 15 syysk. 201919 syysk. 2019
https://www.interspeech2019.org/

Julkaisusarja

NimiInterspeech - Annual Conference of the International Speech Communication Association
ISSN (elektroninen)2308-457X

Conference

ConferenceInterspeech
Maa/AlueItävalta
KaupunkiGraz
Ajanjakso15/09/201919/09/2019
www-osoite

Sormenjälki

Sukella tutkimusaiheisiin 'End-to-End Optimization of Source Models for Speech and Audio Coding Using a Machine Learning Framework'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä