124 citations · 358 across the 27 of their papers we have counts for
15 papers · 1 filter
BandCondiNet: Parallel Transformers-based Conditional Popular Music Generation with Multi-View Features
Jing Luo, Xinyu Yang, Dorien Herremans
Conditional music generation offers significant advantages in terms of user convenience and control, presenting great potential in AI-generated content research. However, building…
Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure
Zixun Guo, Makris Dimos, Herremans Dorien
The rise of deep learning technologies has quickly advanced many fields, including that of generative music systems. There exist a number of systems that allow for the generation o…
AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies
Ha Thi Phuong Thao, Balamurali B. T., Dorien Herremans +1
In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet. We take both audio and video into…
The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy
Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos +1
Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and tra…
A dataset and classification model for Malay, Hindi, Tamil and Chinese music
Fajilatun Nahar, Kat Agres, Balamurali BT +1
In this paper we present a new dataset, with musical excepts from the three main ethnic groups in Singapore: Chinese, Malay and Indian (both Hindi and Tamil). We use this new datas…
Regression-based music emotion prediction using triplet neural networks
Kin Wai Cheuk, Yin-Jyun Luo, Balamurali B +3
In this paper, we adapt triplet neural networks (TNNs) to a regression task, music emotion prediction. Since TNNs were initially introduced for classification, and not for regressi…
Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks
Kin Wai Cheuk, Balamurali B. T., Gemma Roig +1
We present an approach to tackle the speaker recognition problem using Triplet Neural Networks. Currently, the -vector representation with probabilistic linear discriminant anal…
Midi Miner -- A Python library for tonal tension and track classification
Rui Guo, Dorien Herremans, Thor Magnusson
We present a Python library, called Midi Miner, that can calculate tonal tension and classify different tracks. MIDI (Music Instrument Digital Interface) is a hardware and software…
Towards robust audio spoofing detection: a detailed comparison of traditional and learned features
Balamurali BT, Kin Wah Edward Lin, Simon Lui +2
Automatic speaker verification, like every other biometric system, is vulnerable to spoofing attacks. Using only a few minutes of recorded voice of a genuine client of a speaker ve…
Dance Hit Song Prediction
Dorien herremans, David Martens, Kenneth Sörensen
Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the…
MorpheuS: generating structured music with constrained patterns and tension
Dorien Herremans, Elaine Chew
Automatic music generation systems have gained in popularity and sophistication as advances in cloud computing have enabled large-scale complex computations such as deep models and…
A Functional Taxonomy of Music Generation Systems
Dorien Herremans, Ching-Hua Chuan, Elaine Chew
Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical t…
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy
Kin Wah Edward Lin, Balamurali B. T., Enyan Koh +2
Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach insp…
From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec
Ching-Hua Chuan, Kat Agres, Dorien Herremans
We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More p…
Modeling Musical Context with Word2vec
Dorien Herremans, Ching-Hua Chuan
We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to m…