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Found 31 papers, 18 papers with code
KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation
Although being widely adopted for evaluating generated audio signals, the Fr\'echet Audio Distance (FAD) suffers from significant limitations, including reliance on Gaussian assumptions, sensitivity to sample size, and high computational complexity.
TALKPLAY: Multimodal Music Recommendation with Large Language Models
We present TalkPlay, a multimodal music recommendation system that reformulates the recommendation task as large language model token generation.
Sound Scene Synthesis at the DCASE 2024 Challenge
This paper presents Task 7 at the DCASE 2024 Challenge: sound scene synthesis.
Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models
To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding user queries and 2) responding with natural language and retrieved music.
Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation
Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation.
LP-MusicCaps: LLM-Based Pseudo Music Captioning
In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings.
A Demand-Driven Perspective on Generative Audio AI
To achieve successful deployment of AI research, it is crucial to understand the demands of the industry.
Textless Speech-to-Music Retrieval Using Emotion Similarity
We introduce a framework that recommends music based on the emotions of speech.
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results.
Toward Universal Text-to-Music Retrieval
This paper introduces effective design choices for text-to-music retrieval systems.
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
Second, to overcome the absence of existing multi-singing datasets for a training purpose, we present a strategy for construction of multiple singing mixtures using various single-singing datasets.
A Proposal for Foley Sound Synthesis Challenge
"Foley" refers to sound effects that are added to multimedia during post-production to enhance its perceived acoustic properties, e. g., by simulating the sounds of footsteps, ambient environmental sounds, or visible objects on the screen.
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
However, its novelty necessitates a new perspective on how to evaluate such a model.
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Music Classification: Beyond Supervised Learning, Towards Real-world Applications
The target audience for this web book is researchers and practitioners who are interested in state-of-the-art music classification research and building real-world applications.
Listen, Read, and Identify: Multimodal Singing Language Identification of Music
Optionally, LRID-Net is facilitated with modality dropouts to handle a missing modality.
Large-Scale MIDI-based Composer Classification
Music classification is a task to classify a music piece into labels such as genres or composers.
Deep Composer Classification Using Symbolic Representation
In this study, we train deep neural networks to classify composer on a symbolic domain.
Dereverberation using joint estimation of dry speech signal and acoustic system
The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal.
Deep Unsupervised Drum Transcription
We introduce DrummerNet, a drum transcription system that is trained in an unsupervised manner.
Sound Audio and Speech Processing
Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook
Since the vocal component plays a crucial role in popular music, singing voice detection has been an active research topic in music information retrieval.
A Tutorial on Deep Learning for Music Information Retrieval
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research.
A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging
In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks.
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras
We introduce Kapre, Keras layers for audio and music signal preprocessing.
The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging
The results highlight several important aspects of music tagging and neural networks.
Transfer learning for music classification and regression tasks
In this paper, we present a transfer learning approach for music classification and regression tasks.
Convolutional Recurrent Neural Networks for Music Classification
We introduce a convolutional recurrent neural network (CRNN) for music tagging.
Towards Music Captioning: Generating Music Playlist Descriptions
Descriptions are often provided along with recommendations to help users' discovery.
Explaining Deep Convolutional Neural Networks on Music Classification
Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e. g. genre classification, mood detection, and chord recognition.
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN).
Automatic tagging using deep convolutional neural networks
We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs).
Text-based LSTM networks for Automatic Music Composition
In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition.
