Search Results for author:
Found 6 papers, 3 papers with code
Contrastive Learning for Cross-modal Artist Retrieval
We demonstrate our method successfully combines complementary information from diverse modalities, and is more robust to missing modality data (i. e., it better handles the retrieval of artists with different modality embeddings than the query artist's).
Supervised and Unsupervised Learning of Audio Representations for Music Understanding
We find that restricting the domain of the pre-training dataset to music allows for training with smaller batch sizes while achieving state-of-the-art in unsupervised learning -- and in some cases, supervised learning -- for music understanding.
Artist Similarity with Graph Neural Networks
Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music.
Bootstrapping a Music Voice Assistant with Weak Supervision
One of the first building blocks to create a voice assistant relates to the task of tagging entities or attributes in user queries.
Multimodal Metric Learning for Tag-based Music Retrieval
In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
