[제 13회 투빅스 컨퍼런스] OK Mugle! - 장르부터 멜로디까지, Content-based Music Recommendation

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Deep LearningOkMugle
Overview

Ok Mugle! 🎵

장르부터 멜로디까지, Content-based Music Recommendation

발표 ppt(1차)_1

Description 📖

  • 본 프로젝트에서는 Kakao Arena에서 제공하는 Melon Playlist Continuation 데이터를 활용하여, 사용자가 검색한 노래와 유사한 노래 추천을 구현하였습니다.

발표 ppt(1차)_8

  1. [Model] '유사성'의 기준을 멜로디, 분위기, 상황, 장르 등으로 정의
    • 해당 요소 반영하여 Music2Vec, Time Convolutional AutoEncoder, ConsineEmbeddingLoss Multimodal 등의 모델 Building
  2. [Retrieval] Embedding의 Cosine Similarity를 구하여 Retrieval 구성
  3. [Ranking] 다양한 Ranking Method 사용 → 추천 결과 Ensemble
  4. [Serving] 최종적으로 Score Total Top 10 Ranking Method의 추천 결과 활용하여 Web 구현 & 모델 Serving

Usage ✔️

  • Windows Shell에 아래 명령을 입력하여 실행합니다.
set FLASK_APP=server
set FLASK_ENV=development
flask run

Result (Web) 💻

웹 메인

  • 검색창에 '비투비 - 비밀 (Insane) (Acoustic Ver.)'를 검색한 결과 화면

웹 검색결과

Presentation 🙋

컨퍼런스 발표영상과 보고서입니다. 자세한 분석 내용은 아래 링크를 통해 확인해주세요!

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File Directory 📂

Ok Mugle!
├── 1. preprocessig
│   ├── make_song_meta_and_playlist.ipynb       # 노래, 플레이리스트 데이터 전처리
│   ├── make_mel_data.ipynb                     # 멜 데이터 전처리
│   └── make_mel_batch_data.ipynb               # 멜 데이터 배치 단위로 전처리
│
├── 2. model
│   ├── genre_embedding_model.ipynb             # Music2Vec
│   ├── mel_embedding_model.ipynb               # Time Convolutional Autoencoder
│   └── genre_and_mel_embedding_model.ipynb     # CosineEmbeddingLoss Multimodal
│
├── 3. embedding-visualization
│   └── embedding_visualization_tsne.ipynb      # t-SNE를 활용한 각 임베딩별 시각화
│
├── 4. ranking
│   ├── make_ranking_data_preprocessig.ipynb    # 각 임베딩별 코사인 유사도 Top50 데이터 셋 제작 
│   ├── make_ranking_data_multiprocessig.py     # make_ranking_data_preprocessig의 multiprocessig을 위한 함수
│   ├── make_ranking_data.ipynb                 # 순위별 가중치 ranking, 각 임베딩 별 상위 Top3 ranking
│   └── cos_sim_music_serving.ipynb             # 각 임베딩, ranking 별 결과
│
└── 5. web
    ├── crawling                                # 결과창 구현을 위한 데이터 수집
    │   └── melon_crawling.py 
    │ 
    ├── data                                    # 웹 제작에 활용된 데이터
    │    ├── ranking_song_id2playlist.json
    │    ├── song_id2artist_name_basket.json
    │    ├── song_id2song_name.json
    │    └── song_name_artist_name2song_id.json
    │ 
    ├── static                                  # 웹 제작에 활용된 css, font, image, js
    │    ├── css
    │    ├── fonts
    │    ├── images
    │    └── js
    │ 
    ├── templates                               # 프론트 구현
    │    ├── about.html
    │    ├── index.html
    │    ├── people.html
    │    └── result.html
    │ 
    └── server.py                               # 백엔드 구현
    │
    └── requirements.txt                        # 필요 패키지 목록
      
Owner
SeongBeomLEE
안녕하세요.👋 같이에 가치를 아는 머신러닝 엔지니어 이성범 입니다!
SeongBeomLEE
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