التفاصيل البيبلوغرافية
| العنوان: |
Deep embeddings with Essentia models |
| المؤلفون: |
Alonso-Jiménez, Pablo, Bogdanov, Dmitry, Serra, Xavier |
| بيانات النشر: |
ISMIR |
| سنة النشر: |
2020 |
| المجموعة: |
UPF Digital Repository (Universitat Pompeu Fabra, Barcelona) |
| الوصف: |
Comunicació presentada a: International Society for Music Information Retrieval Conference celebrat de l'11 al 16 d'octubre de 2020 de manera virtual. ; We present the integration of various CNN TensorFlow models developed for different MIR tasks into Essentia. This is a continuation of our previous work [1], extending the list of supported models and adding new algorithms to facilitate usability. Essentia provides input feature extraction and inference with TensorFlow models in a single C++ pipeline with Python bindings, facilitating the deployment of C++ and Python MIR applications. We assess the new models’ capabilities to serve as embedding extractors in many downstream classification tasks. All presented models are publicly available on the Essentia website. |
| نوع الوثيقة: |
conference object |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
http://hdl.handle.net/10230/45452 |
| الاتاحة: |
http://hdl.handle.net/10230/45452 |
| Rights: |
Licensed under a Creative Commons Attribution 4.0 In- ternational License (CC BY 4.0). 21st International Society for Music Information Retrieval Conference, Montréal, Canada, 2020. ; https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.E71174EE |
| قاعدة البيانات: |
BASE |