EURASIP Journal on Audio Speech and Music Processing

Scope & Guideline

Bridging science and sound through rigorous research.

Introduction

Welcome to the EURASIP Journal on Audio Speech and Music Processing information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of EURASIP Journal on Audio Speech and Music Processing, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1687-4722
PublisherSPRINGER
Support Open AccessYes
CountryUnited States
TypeJournal
Convergefrom 2007 to 2024
AbbreviationEURASIP J AUDIO SPEE / EURASIP J. Audio Speech Music Process.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The EURASIP Journal on Audio Speech and Music Processing focuses on the interdisciplinary fields of audio, speech, and music processing, emphasizing both theoretical advancements and practical applications. This journal serves as a platform for researchers and practitioners to disseminate their findings in these areas, with a strong emphasis on innovative techniques and methodologies.
  1. Audio Signal Processing:
    Research on techniques for processing audio signals, including enhancement, separation, and synthesis methods, often leveraging advanced machine learning and deep learning techniques.
  2. Speech Processing:
    Studies focused on automatic speech recognition, speech synthesis, emotion recognition, and speaker identification, exploring both traditional and modern computational methods.
  3. Music Information Retrieval (MIR):
    Investigations into algorithms and systems for extracting meaningful information from music, including music generation, classification, and recommendation systems.
  4. Acoustic Scene Analysis:
    Research on the recognition and analysis of different acoustic environments, including sound source localization and classification, often using sensor networks and advanced signal processing techniques.
  5. Machine Learning Applications:
    Utilization of machine learning, particularly deep learning, in various audio and speech applications, emphasizing innovative architectures and training strategies.
  6. Sensor Networks and Distributed Systems:
    Exploration of distributed acoustic sensor networks for applications such as sound field reconstruction and environmental monitoring, focusing on algorithmic and architectural innovations.
Recent publications in the journal highlight several emerging themes that indicate a shift in research focus towards more advanced and interdisciplinary approaches. These themes are gaining traction and are likely to shape the future of audio, speech, and music processing research.
  1. AI and Machine Learning Innovations:
    There is a significant increase in research exploring the application of artificial intelligence and machine learning techniques, particularly deep learning, for various audio and speech processing tasks.
  2. Cross-Modal Processing:
    Emerging studies focus on cross-modal techniques that integrate visual and auditory information, enhancing the capabilities of systems for tasks such as speech recognition and sound event detection.
  3. Real-Time Processing Applications:
    A surge in research targeting real-time processing capabilities for applications such as live speech enhancement and interactive music systems reflects the demand for immediate and responsive audio solutions.
  4. Acoustic Scene Understanding and Localization:
    An increasing emphasis on understanding and localizing sound sources within complex acoustic environments, often utilizing sensor networks and advanced computational models.
  5. Generative Models in Music and Audio:
    The use of generative models for music composition and audio synthesis is trending, with researchers exploring novel approaches to generate complex musical structures and soundscapes.

Declining or Waning

While the journal has seen a surge in various research themes, certain areas appear to be declining in prominence. These waning scopes may reflect shifts in research interests or advancements in technology that make older approaches less relevant.
  1. Traditional Signal Processing Techniques:
    There is a noticeable decline in studies focusing solely on traditional signal processing methods, as the field has increasingly shifted towards machine learning and deep learning approaches.
  2. Low Resource Language Processing:
    Research specifically targeting low resource languages, while still relevant, has decreased, possibly due to the growing focus on methods applicable to a broader range of languages and dialects.
  3. Basic Audio Feature Extraction:
    Papers dedicated to basic audio feature extraction techniques have become less frequent, as newer methodologies incorporating advanced machine learning techniques take precedence.
  4. Acoustic Echo Cancellation:
    Although still a critical area, the frequency of publications solely focused on acoustic echo cancellation has diminished, potentially due to advancements in integrated solutions that combine multiple processing tasks.
  5. Manual Music Transcription:
    The area of manual or semi-automated music transcription has seen a reduction in focus, likely overshadowed by more sophisticated automated methods powered by deep learning.

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