{"id":5150,"date":"2018-09-25T08:23:28","date_gmt":"2018-09-25T08:23:28","guid":{"rendered":"https:\/\/athis-consulting.com\/news\/?p=5150"},"modified":"2018-10-02T10:26:40","modified_gmt":"2018-10-02T10:26:40","slug":"deezers-ai-can-classify-songs-by-mood-and-intensity","status":"publish","type":"post","link":"https:\/\/athis-technologies.com\/news\/innovation\/ai-big-data\/2018\/deezers-ai-can-classify-songs-by-mood-and-intensity\/","title":{"rendered":"Deezer\u2019s AI can classify songs by mood and intensity"},"content":{"rendered":"

In an attempt to get the latter to perform a bit more like the former, researchers at Deezer developed an artificially intelligent system that can associate certain tracks with moods. They describe their work in a new paper (\u201cMusic Mood Detection Based on Audio Lyrics With Deep Neural\u00a0Nets<\/a>\u201c) published on the preprint server Arxiv.org<\/p>\n

“Automatic music mood detection has been an active field of research \u2026 for the past twenty years,\u201d they wrote. \u201cIt consists of automatically determining the emotion felt when listening to a track. In this work, we focus on the task of multimodal mood detection based on the audio signal and the lyrics of the track.\u201d<\/p><\/blockquote>\n

The team, citing psychological studies suggesting that lyrics \u201cshould be jointly considered\u201d when analyzing musical mood, designed a neural network they separately fed audio signals and\u00a0word2vec embeddings<\/a>\u00a0trained on 1.6 million lyrics. To teach it to gauge songs\u2019 emotional resonance, they tapped the Million Song Dataset (MSD) \u2014 a database of tracks associated with tags from LastFM, some of which relate to moods \u2014 and 14,000 English words with embeddings ranging in valence (from negative to positive) and arousal (from calm to energetic), which they used to select the aforementioned tags for training.<\/p>\n

Because the MSD doesn\u2019t include audio signals and lyrics, the team mapped it with Deezer\u2019s catalog using song metadata \u2014 specifically the song title, artist name, and album title. And they extracted words from lyrics at the corresponding location relative\u00a0to\u00a0the length of the lyrics.<\/p>\n

About 60 percent of the resulting dataset \u2014 18,644 annotated tracks in all \u2014 was used to train the model, with 40 percent reserved for validation and testing.<\/p>\n

Compared to classical systems that draw on lexicons related to emotion, the deep learning model was superior\u00a0in arousal detection. When it came to valence detection, the results were more of a mixed bag \u2014 the researchers note that lyrics-based methods in deep learning tend to perform poorly \u2014 but it still managed to match the performance of\u00a0feature engineering-based approaches<\/a>.<\/p>\n

\u201cIt seems that this gain of performance is the result of the capacity of our model to unveil and use mid-level correlations between audio and lyrics, particularly when it comes to predicting valence,\u201d the researchers wrote. \u201cStudying and optimizing in detail ConvNets for music mood detection offers the opportunity to temporally localize zones responsible for the valence and arousal of a track.\u201d<\/p><\/blockquote>\n

They suggest that subsequent research could use a database with labels to indicate the degree of ambiguity in the mood\u00a0of tracks, or leverage an unsupervised model trained on high volumes of unlabeled data. Both tacks, they contend, would \u201cimprove significantly\u201d the prediction accuracy of future models.<\/p>\n","protected":false},"excerpt":{"rendered":"

In an attempt to get the latter to perform a bit more like the former, researchers at Deezer developed an artificially intelligent system that can associate certain tracks with moods. They describe their work in a new paper (\u201cMusic Mood Detection Based on Audio Lyrics With Deep Neural\u00a0Nets\u201c) published on the preprint server Arxiv.org “Automatic […]<\/p>\n","protected":false},"author":5,"featured_media":5154,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"amp_status":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[208,241],"tags":[127,396,297,397],"jetpack_publicize_connections":[],"yoast_head":"\nDeezer\u2019s AI can classify songs by mood and intensity - AthisNews<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/athis-technologies.com\/news\/innovation\/ai-big-data\/2018\/deezers-ai-can-classify-songs-by-mood-and-intensity\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deezer\u2019s AI can classify songs by mood and intensity - AthisNews\" \/>\n<meta property=\"og:description\" content=\"In an attempt to get the latter to perform a bit more like the former, researchers at Deezer developed an artificially intelligent system that can associate certain tracks with moods. 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