192,570 research outputs found
Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval
Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pre-trained Doc2Vec model followed by fully-connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. ii) As for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval
Audio-Visual Embedding for Cross-Modal MusicVideo Retrieval through Supervised Deep CCA
Deep learning has successfully shown excellent performance in learning joint
representations between different data modalities. Unfortunately, little
research focuses on cross-modal correlation learning where temporal structures
of different data modalities, such as audio and video, should be taken into
account. Music video retrieval by given musical audio is a natural way to
search and interact with music contents. In this work, we study cross-modal
music video retrieval in terms of emotion similarity. Particularly, audio of an
arbitrary length is used to retrieve a longer or full-length music video. To
this end, we propose a novel audio-visual embedding algorithm by Supervised
Deep CanonicalCorrelation Analysis (S-DCCA) that projects audio and video into
a shared space to bridge the semantic gap between audio and video. This also
preserves the similarity between audio and visual contents from different
videos with the same class label and the temporal structure. The contribution
of our approach is mainly manifested in the two aspects: i) We propose to
select top k audio chunks by attention-based Long Short-Term Memory
(LSTM)model, which can represent good audio summarization with local
properties. ii) We propose an end-to-end deep model for cross-modal
audio-visual learning where S-DCCA is trained to learn the semantic correlation
between audio and visual modalities. Due to the lack of music video dataset, we
construct 10K music video dataset from YouTube 8M dataset. Some promising
results such as MAP and precision-recall show that our proposed model can be
applied to music video retrieval.Comment: 8 pages, 9 figures. Accepted by ISM 201
FMA: A Dataset For Music Analysis
We introduce the Free Music Archive (FMA), an open and easily accessible
dataset suitable for evaluating several tasks in MIR, a field concerned with
browsing, searching, and organizing large music collections. The community's
growing interest in feature and end-to-end learning is however restrained by
the limited availability of large audio datasets. The FMA aims to overcome this
hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio
from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a
hierarchical taxonomy of 161 genres. It provides full-length and high-quality
audio, pre-computed features, together with track- and user-level metadata,
tags, and free-form text such as biographies. We here describe the dataset and
how it was created, propose a train/validation/test split and three subsets,
discuss some suitable MIR tasks, and evaluate some baselines for genre
recognition. Code, data, and usage examples are available at
https://github.com/mdeff/fmaComment: ISMIR 2017 camera-read
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