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APSIPA Transactions on Signal and Information Processing is a peer-reviewed journal published by Cambridge University Press, covering Speech and Audio Processing, Speech Recognition and Synthesis, Music and Audio Processing.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, APSIPA Transactions on Signal and Information Processing 12 (2023) 45–58.
Formats any DOI in the closest standard style — APSIPA Transactions on Signal and Information Processing has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Speech and Audio Processing Speech Recognition and Synthesis Music and Audio Processing Advanced Vision and Imaging Video Coding and Compression Technologies |
| ISSN | 2048-7703 |
| Citation impact (2-yr) | 3.8 |
| h-index | 38 |
| i10-index | 119 |
| Total citations | 7,540 |
| Article processing charge | $1,000 |
| Open access | Yes |
| Top institutions publishing here | University of Southern California |
| Journal website | www.nowpublishers.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the…
d. graziosi, 1 o. nakagami, 2 s. kuma, 2 a. zaghetto, 1 t. suzuki 2 and a. tabatabai 1
The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of…
Recently, the speech community is seeing a significant trend of moving from deep neural network based hybrid modeling to end-to-end (E2E) modeling for automatic speech recognition (ASR). While E2E models achieve the state-of-the-art results in most benchmarks in terms of ASR accuracy, hybrid models are still used in a large proportion of commercial ASR systems…
Extensive evaluation on a large number of word embedding models for language processing applications is conducted in this work. First, we introduce popular word embedding models and discuss desired properties of word models and evaluation methods (or evaluators). Then, we categorize evaluators into intrinsic and extrinsic two types. Intrinsic evaluators test the quality of a…
