  Word embedding has become an essential means for text-based information
retrieval. Typically, word embeddings are learned from large quantities of
general and unstructured text data. However, in the domain of music, the word
embedding may have difficulty understanding musical contexts or recognizing
music-related entities like artists and tracks. To address this issue, we
propose a new approach called Musical Word Embedding (MWE), which involves
learning from various types of texts, including both everyday and music-related
vocabulary. We integrate MWE into an audio-word joint representation framework
for tagging and retrieving music, using words like tag, artist, and track that
have different levels of musical specificity. Our experiments show that using a
more specific musical word like track results in better retrieval performance,
while using a less specific term like tag leads to better tagging performance.
To balance this compromise, we suggest multi-prototype training that uses words
with different levels of musical specificity jointly. We evaluate both word
embedding and audio-word joint embedding on four tasks (tag rank prediction,
music tagging, query-by-tag, and query-by-track) across two datasets (Million
Song Dataset and MTG-Jamendo). Our findings show that the suggested MWE is more
efficient and robust than the conventional word embedding.
