Content-based Representations of audio using Siamese neural networks
This addresses the problem of retrieving similar audio recordings for users in audio databases, though it is incremental as it applies an existing neural network method to audio data.
The paper tackles content-based audio retrieval by proposing Siamese Neural Networks to encode audio into vector representations, enabling effective retrieval of semantically similar audio using simple similarity measures like Euclidean distance and cosine similarity.
In this paper, we focus on the problem of content-based retrieval for audio, which aims to retrieve all semantically similar audio recordings for a given audio clip query. This problem is similar to the problem of query by example of audio, which aims to retrieve media samples from a database, which are similar to the user-provided example. We propose a novel approach which encodes the audio into a vector representation using Siamese Neural Networks. The goal is to obtain an encoding similar for files belonging to the same audio class, thus allowing retrieval of semantically similar audio. Using simple similarity measures such as those based on simple euclidean distance and cosine similarity we show that these representations can be very effectively used for retrieving recordings similar in audio content.