eschae / similarsoundsearch

A small command line query-by-example search engine for similar sounds.

SimilarSoundSearch

A small command line query-by-example search engine for similar sounds.

This repository contains the code and evaluation data related to a bachelor's thesis written at the University of Freiburg. A content-based search algorithm for similar sounding sounds was implemented and evaluated using crouwdsourcing.

Given an audiofile as query and a specific database with sounds the algorithm returns the most similar sounds to the query. You can directly listen to the sounds from the terminal.

Usage

To perform a search you need to call similarsounds.py from the terminal. Given a soundbase and a query the program will return a list of most similar sounds to the query found in the soundbase. If no query is specified, a randomly selected sound from the soundbase will be used. Once presented with the results you can decide if you want to listen to the sounds directly from the terminal, perform a new query or exit.

Click on the picture below to see how it works (you will be redirected to the video - don't forget to turn your speakers or headphones on!):

If you want to customize your search check

$ python similarsounds.py -h

for more options.

If using the example database is not enough for you and you want to build your own just try

$ python soundbase.py -h

Requirements

Up to now the functionality was only tested on Ubuntu 14.04.

Linux

• python 2.x (used version is 2.7.6., theres no support yet for python 3 because of missing support for python 3 within Essentia (see MTG/essentia#138)

• Essentia (v.2.1._beta2 _fixes) for feature extraction. You can download it from here for a complete installation follow the instructions here. For SimilarSoundSearch it should be enough to do the following:

Install the dependencies

sudo apt-get install build-essential libfftw3-dev libavcodec-dev libavformat-dev libavutil-dev libavresample-dev python-dev libsamplerate0-dev python-numpy-dev python-numpy

Then go into the source directory and configure with

./waf configure --mode=release --with-python

compile

./waf

and insall (you might need sudo).

./waf install

Additional python packages:

• scikit-learn for nearest neighbors search (requires numpy and scipy)

• dataset 0.6.0 for sql workarounds

• tqdm for displaying a nice progressbar

$ pip install scikit-learn dataset tqdm

For listen to audiofiles directly from terminal:

• SoX and a handler for mp3 files

$ sudo apt-get install sox libsox-fmt-mp3

similarsoundsearch's Issues

No documentation for training.

The tutorial video repeats the same single process of playing an mp3 from the DB for eleven minutes. Training and parameters are not showcased, nor there is any detail as to which of the Essentia clustering methods (all pretty outdated) you decided to implement for your experiment.
It'd be good to have more examples to provide you with useful feedback.