Science AI Journal
Computer Science · Research topic

Open research questions in Music and Audio Processing

94 gap statements mined from Music and Audio Processing papers in our 4.5M-paper local library, which holds 599 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

Choosing where to publish on Music and Audio Processing? See the ranked Computer Science journals.

What the literature leaves open

  • The "Opposite Song Generation Process" is introduced to address the challenges associated with music exploration, such as breaking free from recommendation loops and encouraging users to discover songs outside their comfort zones. By deliberately selecting songs with contrasting characteristics, the model enriches the user experience, promoting better exploration of musical tastes.

    A Fuzzy C Means Clustering and Cosine Similarity-Based Song Recommender System · 2026 · DOI

  • Future work may involve incorporating recurrent neural networks (RNNs) for better temporal feature analysis. The system can be extended to support multilingual audio analysis and real-time streaming voice authentication. Additional biometric features such as speaker emotion, speech rhythm, and voice behavior analysis can be incorporated to strengthen authentication capability.

    Deep Fake Audio Detection Using Deep Learning · 2026 · DOI

  • The framework is limited to analyzing sonata form and may not be applicable to other music forms.

    Sonalyzer-Moz: A Framework for Analyzing the Structure of Mozart's Sonata Form · 2026

  • The gap is that auditory boundary benchmarks are often written as classifier evaluations, which do not capture the semantic and operational aspects of boundary behavior.

    Executable Boundary Contracts for Sound Event Traces · 2026

  • The lack of effective methods for Environmental Sound Classification in data-constrained scenarios. The need for more accurate and efficient ESC systems.

    Comparing Audio and Visual Transfer Learning for Environmental Sound Classification · 2026 · DOI

  • The conventional technique of recommending music does not take into account the current emotional status of the user. Human emotions are intricate and dynamic, and algorithms relying on one data source are insufficient to represent humans' emotional states.

    Multi-Modal Emotion Based Music Recommendation System · 2026 · DOI

  • To enable real-time deployment in user-facing applications such as streaming music platforms or emotion-aware recommendation systems, future work could explore lightweight variants of our architecture using model pruning, quantization, or knowledge distillation. Techniques such as domain adaptation, few-shot learning, or semi-supervised multimodal training could be explored in this context.

    A deep learning framework for emotion recognition in music using multimodal data fusion · 2026 · DOI

  • Future research could employ silhouette analysis or elbow-method diagnostics to discover the unconstrained optimal clustering structure of the catalogue.

    Quantitative Evaluation of Stylistic Consistency and Acoustic Evolution: A Case Study of Jay Chou's Career from 2000 to 2026 · 2026 · DOI

  • This paper addresses critical limitations in existing music auto-tagging research by proposing MuCoGraph, a novel multimodal graph-based framework that comprehensively integrates content-based and social interaction features.

    A multimodal graph-based music auto-tagging framework: integrating social and content intelligence · 2026 · DOI

  • Generation latency and unpredictable outputs remain challenges for neural text-to-audio systems. The tweakability problem for neural audio systems remains a challenge. Sharp stylistic pivots and timbral specificity are difficult to achieve.

    A Text-Steerable Instrument for Sketching Procedural Soundscapes via Language Models · 2026 · DOI

  • The system struggles with sharp stylistic pivots and cannot match timbral specificity for prompts like 'upright bass'. Retrieval coverage gaps cause occasional semantic mismatches. The system requires a one-time dataset construction step to amortize LLM cost.

    A Text-Steerable Instrument for Sketching Procedural Soundscapes via Language Models · 2026 · DOI

  • Future studies should utilize large and heterogeneous datasets to enhance robustness and generalization for MGC in diverse acoustic scenarios. The proposed system can be extended to incorporate additional modalities, such as lyrics and cultural cues, to improve its contextual understanding of genre semantics.

    Joint spectro-temporal and perceptual feature learning using a dual-track attention network for music genre classification · 2026 · DOI

  • The results are limited to the specific set of data and experimental setup, but the proposed temporal-style feature engineering framework offers a good starting point for further melody similarity prediction research with larger datasets, artist independent evaluation protocols and perceptually validated similarity annotations.

    Explicit encoding of temporal dynamics for interpretable melody similarity modeling: insight into machine learning models · 2026 · DOI

  • The paper identifies a gap in the existing literature, as many of the features highlighted by the model are not discussed at all in the existing literature. The paper aims to fill this gap by providing a computational approach to the analysis of art.

    Machine learning of artistic fingerprints in jazz · 2026 · DOI

  • To create a dataset that can be generated and versioned fully from a single data source.

    An Intelligent Music Recommendation System Using Machine Learning and User Preference Analysis · 2026 · DOI

  • The existing music recommendation systems do not incorporate advanced machine learning techniques. The existing systems do not provide personalized recommendations based on user preferences.

    An Intelligent Music Recommendation System Using Machine Learning and User Preference Analysis · 2026 · DOI

  • Long-term acoustic analysis of individual artists remains relatively limited in Mandopop studies. Longitudinal quantitative studies of individual artists in Mandopop remain relatively limited.

    Quantitative Evaluation of Stylistic Consistency and Acoustic Evolution: A Case Study of Jay Chou's Career from 2000 to 2026 · 2026 · DOI

  • The GTZAN dataset used in this study has limited environmental and scale diversity. The proposed system primarily focuses on the audio modality for MGC, which may limit its contextual understanding of genre semantics. The model does not account for various sources of background noise and multiple instruments overloading, which can affect its performance in real-world scenarios.

    Joint spectro-temporal and perceptual feature learning using a dual-track attention network for music genre classification · 2026 · DOI

  • Environmental sound recognition (ESR) enables listeners to interpret complex acoustic environments, yet the frequency regions that support recognition are poorly understood.

    Estimating band importance for environmental sound recognition using deep learning · 2026 · DOI

  • Designing frameworks tailored to the musical features of sonata form. Increasing the size and diversity of the SoSA-Moz dataset. Exploring the application of the Sonalyzer-Moz framework to other music forms.

    Sonalyzer-Moz: A Framework for Analyzing the Structure of Mozart's Sonata Form · 2026

  • Evaluating the effectiveness of Visual Transfer Learning on other datasets and domains. Comparing the performance of Visual Transfer Learning to other approaches, such as traditional machine learning methods.

    Comparing Audio and Visual Transfer Learning for Environmental Sound Classification · 2026 · DOI

  • To improve the accuracy of the emotion detection algorithms. To incorporate more modalities, such as physiological signals, into the system. To develop a more personalized and adaptive music recommendation system.

    Multi-Modal Emotion Based Music Recommendation System · 2026 · DOI

  • The difficulty of soundchecking in small venues - The need for a listener with music experience - The challenge of scaling the framework to other instruments and control…

    Automatic Live Music Soundchecking with Reference Audio on a Laptop · 2026 · DOI

  • Testing the system in a real-world environment - Scaling the framework to other instruments and control parameters - Improving the model architecture and perceptual…

    Automatic Live Music Soundchecking with Reference Audio on a Laptop · 2026 · DOI

  • The complexity of Indian classical music - The limited availability of labeled datasets - The need for accurate and efficient methods for raga…

    RagaDetNet: deep learning based Indian classical raga recognition using improved Archimedes optimization algorithm for acoustic feature selection · 2026 · DOI

Most-cited papers in Music and Audio Processing

Most recent work

Find a gap in your own Music and Audio Processing sub-topic

This page shows what the Music and Audio Processing literature already flags as unresolved. To narrow it to your specific question, search the Research Gap Finder: the search is free with a free account and lists the papers closest to your topic first. Unlocking that topic (50 credits, charged once) fills the comparison table from our 4.5M-paper local library and writes the gaps from its rows.

Open the Research Gap Finder →

Related topics in Computer Science

Online Learning and Analytics
887 open questions
AI in Service Interactions
805 open questions
Quantum Computing Algorithms and Architecture
632 open questions
Virtual Reality Applications and Impacts
610 open questions
Teaching and Learning Programming
576 open questions
Digital literacy in education
522 open questions
Computational Drug Discovery Methods
505 open questions
Topic Modeling
482 open questions
Open questions raised by papers listed on this page include: (1) Future work may involve incorporating recurrent neural networks (RNNs) for better temporal feature analysis (“Deep Fake Audio Detection Using Deep Learning”, 2026); (2) The lack of effective methods for Environmental Sound Classification in data-constrained scenarios (“Comparing Audio and Visual Transfer Learning for Environmental Sound…”, 2026); (3) Future research could employ silhouette analysis or elbow-method diagnostics to discover the unconstrained optimal clustering structure of the catalogue (“Quantitative Evaluation of Stylistic Consistency and Acoustic Evolution: A Case…”, 2026); (4) Future studies should utilize large and heterogeneous datasets to enhance robustness and generalization for MGC in diverse acoustic scenarios (“Joint spectro-temporal and perceptual feature learning using a dual-track…”, 2026). 94 gap statements have been mined from Music and Audio Processing papers in our 4.5M-paper local library, which holds 599 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 120,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? Acceptance odds for top, mid and open/emerging journals, in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck for prior publication across 7 sources and our local library.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

How Pre-Check worksHow AI Review works

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.