EURASIP Journal on Audio Speech and Music Processing
Scope & Guideline
Empowering researchers to shape the audio landscape.
Introduction
Aims and Scopes
- 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. - Speech Processing:
Studies focused on automatic speech recognition, speech synthesis, emotion recognition, and speaker identification, exploring both traditional and modern computational methods. - Music Information Retrieval (MIR):
Investigations into algorithms and systems for extracting meaningful information from music, including music generation, classification, and recommendation systems. - 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. - Machine Learning Applications:
Utilization of machine learning, particularly deep learning, in various audio and speech applications, emphasizing innovative architectures and training strategies. - 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.
Trending and Emerging
- 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. - 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. - 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. - 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. - 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
- 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. - 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. - 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. - 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. - 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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