About the Journal
The Journal of Music and Language Technology is dedicated to publishing cutting-edge research at the intersection of music and language, with a focus on technological applications and advancements. Our primary aim is to bridge these two complex domains by exploring how computational and analytical methods can be applied to both music and language data. We welcome interdisciplinary research that leverages insights from across a wide range of fields, including computer science, musicology, cognitive science, electrical engineering, and library & information science.
The journal's scope is broad, covering both theoretical and applied research. We invite submissions on the following topics:
Music and Audio Technology
Music Information Retrieval (MIR): Processing, analyzing, organizing, and accessing music information. This includes, but is not limited to, music transcription, genre classification, recommendation systems, and similarity searches.
Audio Signal Processing: Techniques for manipulating and analyzing sound, such as source separation, feature extraction, and audio-based emotion recognition.
Music Technology: The development and application of tools for music creation, production, and analysis.
Spoken Language Processing
This section is a prominent theme of the journal. We welcome submissions on core tasks and their subtasks, including:
Automatic Speech Recognition (ASR): The conversion of spoken words into written text. This includes robust ASR in noisy environments, accented speech recognition, and automatic lyric transcription.
Speech Synthesis: Generating human-like speech from text.
Speaker Recognition: The identification of individuals based on their voice. This encompasses both speaker verification (confirming a speaker's identity) and speaker identification (determining who a speaker is from a set of known speakers).
Speech Enhancement: Techniques to improve the quality of speech signals, for instance, by reducing noise or reverberation.
Speaker Diarization: The process of identifying "who spoke when" in an audio recording.
Speech Translation: Directly translating spoken language from one language to another.
Spoken Language Understanding (SLU): Analyzing spoken utterances to determine their meaning. This includes tasks like intent classification and dialog act classification.
Language Technology and Processing
Linguistic Analysis: Studies on phonology, morphology, and syntax, including methods for chunking/shallow parsing and full parsing/grammatical formalisms.
Semantic Processing: Research on lexical semantics, ontology, and other methods for understanding meaning, such as sentiment analysis, paraphrasing, and entailment.
Text and Data Mining: Information retrieval, information extraction, and other techniques for discovering patterns and knowledge from large text corpora.
Natural Language Applications: The development of advanced applications like machine translation, question answering, and dialog systems.
Interdisciplinary Methods
Statistical and Knowledge-based Methods: The use of both statistical learning models and knowledge-based systems for music and language processing.
Linguistic Resources: The creation and management of corpora, lexicons, and other resources that facilitate research in both fields.
Our journal seeks to be a central platform for researchers and practitioners who are pushing the boundaries of what is possible in music and language technology. By fostering a dialogue between these fields, we aim to inspire new innovations and enhance our understanding of human expression and communication.