Similarity Measures For Vocal-based Drum Sample Retrieval Using Deep Convolutional Auto-encoders

Abstract

The expressive nature of the voice provides a powerful medium for communicating sonic ideas, motivating recent research on methods for query by vocalisation. Meanwhile, deep learning methods have demonstrated state-of-the-art results for matching vocal imitations to imitated sounds, yet little is known about how well learned features represent the perceptual similarity between vocalisations and queried sounds. In this paper, we address this question using similarity ratings between vocal imitations and imitated drum sounds. We use a linear mixed effect regression model to show how features learned by convolutional auto-encoders (CAEs) perform as predictors for perceptual similarity between sounds. Our experiments show that CAEs outperform three baseline feature sets (spectrogram-based representations, MFCCs, and temporal features) at predicting the subjective similarity ratings. We also investigate how the size and shape of the encoded layer effects the predictive power of the learned

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keymehrabi2018similarity

Related papers

• Deep Embeddings For Robust User-based Amateur Vocal Percussion Classification (2022)0.00
• A Comparative Re-assessment Of Feature Extractors For Deep Speaker Embeddings (2020)8.09
• A Recurrent Encoder-decoder Approach With Skip-filtering Connections For Monaural Singing Voice Separation (2017)9.41
• Robust Vocal Quality Feature Embeddings For Dysphonic Voice Detection (2022)7.16
• Timbre Transfer With Variational Auto Encoding And Cycle-consistent Adversarial Networks (2021)0.00
• Examining The Mapping Functions Of Denoising Autoencoders In Singing Voice Separation (2019)8.35
• Conditional Deep Hierarchical Variational Autoencoder For Voice Conversion (2021)0.00
• A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion (2023)0.00