Week 1 (1/7/19)

Data Decisions

Muiredach O’Riain

week: 1
Starting: 1st July 2019 – 1/7/19

My main plan for this week was deciding
Deciding Upon and Defining the Relevant Features to Classify
Finding relevant Impulse Responses
Finding a series of clean sounds to be convolved as a basis for the training data


The problem of finding suitable Impulse Responses solved thanks to the Aachen Impulse Response (AIR) Database, a huge and well documented collection of IR recordings made by RWTH Aachen University in Germany. The IRs are systematically labelled with a range of features that are suitable to be used in this project. They are stored as double-precision binary floating-point MAT-files which can be imported directly into MATLAB, however they can also be extracted in python using scipy.io.loadmat().


To get the dry audio sample sounds I went to https://www.pacdv.com/sounds, a website that hosts a huge amount of free to use non-copyrighted sound files for use in video, film, audio and multimedia productions.
I used GNU Wget, a computer program that retrieves content from web servers, to retrieve all the wav files automatically and store them on a Hard Disk Drive.

Since I had already gathered a lot of Data I began running some tests in python;

Convolution-
I Used scipy.signal.convolve to convolve two signals, the signals were convolved but when played back there was distortion

Extraction-
Using scipy.io.loadmat and wave libraries for python I managed to extract an IR from the .mat files as a .wav however the audio was heavily distorted, need to figure out the right settings.

Goals for next week

Storage & Labelling

  • labels based on the type of sounds, rooms they are being simulated in etc.
  • Creating an organised system for storing and accessing created data as well as the IRs and any other relevant data or coding projects.


Processing the samples

  • Either finding or beginning to design a suitable tool/plug-in that can convolve our clean samples with the IRs decided upon


Convolving the samples and generating our data

  • using the tool to process all of the sound files to simulate them in the spaces chosen

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