Average Model Cost: $0.0035
Number of Runs: 25,233
Models by this creator
The effnet-discogs model is an EfficientNet model that has been trained to classify music styles based on audio inputs. It can assign one of 400 different music styles from the Discogs taxonomy to an audio clip. This model has been efficiently designed to provide accurate and fast music style classification for various applications.
The music-arousal-valence model is a regression model that predicts the arousal and valence values of music. Arousal represents the level of energy or excitement in the music, while valence represents the emotional positivity or negativity of the music. The model takes in audio features of the music and outputs numerical values for arousal and valence. This model can be used in applications that require the analysis or recommendation of music based on its emotional characteristics.
music-classifiers is a collection of transfer learning models for music classification, specifically for genres, moods, and instrumentation. These models are designed to leverage pre-trained models and transfer the knowledge to classify music in various categories.
The model is designed to classify music approachability and engagement. It takes audio input of music and uses a classification algorithm to determine how approachable and engaging the music is. The model can provide insights into the characteristics of music that make it more likely to be enjoyed or preferred by listeners. This can be useful in various applications such as music recommendation systems or market research for music platforms.