Scripts to support experimental study carried out in The Department of Sound Engineering of Fryderyk Chopin University of Music. The objective of the study was to measure listener’s reaction time in the recognition of the sources of environmental sounds embedded in various acoustic sceneries. The secondary objective of the study was to determine whether people with musical (hearing training) education background recognize the sounds faster than non-musicians and whether the sound recognition reaction time is influenced by the congruency of the sound and the scenery in which the sound is presented.
The study produces two tracks of recorded audio files: one with recorded prompts that were played over ambient acoustic scenery (prompts) other with recorded audio file of listeners' responses naming the prompts (responses). The experiment needs tagging of both & measuring the time distance between appropriate prompts & responses and the cognition's accuracy.
Each file has to be listened by auditor and appropriate tags have to be placed around prompts & responses. This takes huge amount of time.
Process both prompts & responses audio files with software to identify as much information as possible.
The script does three things: traces prompts audio file & inserts tags to mark prompt's occurrence, traces responses audio file & tags all answers, does speech recognition to eliminate non-valid answers in the survey recording.
Requirements: python 3.5+, sox binary in path, python libraries: wave, audioop and SpeechRecognition.
Script requires one parameter: a name of the prompts audio file:
python wav2time.py data/efekty-11_24_1033.wavThe output result is a Audacity acceptable labels format, an UTF-8 text file containing three columns: beginning of the tag (in seconds), end of the tag (in seconds), class (prompt # or answer #) combined with recognized response:
0.875011 2.746757 prompt #1:
8.503673 10.378413 prompt #2:
17.558095 19.543583 prompt #3:
1.594376 2.418503 answer #1: telefon
9.060408 10.009252 answer #2: gwizdek
18.804263 19.800000 answer #3: chodzenie
The name of the output file in the example would be:
data/etykiety-in-11_24_1033.txtThe file is Audacity acceptable labels format.
The script takes the output of the middle stage of the workflow, reviewed tags from Audacity and calculates delay between appropriate prompts and answers & outputs it in the form of CSV table.
Requirements: Python 3.5+.
Script requires one parameter: a name of the audited tags file:
python time2csv.py data/etykiety-out-11_24_1033.txtThe name of the output file in the example would be:
data/etykiety-in-11_24_1033.csvAnd the contents in the above example would be (also see Workflow below):
Id [#];Delay [ms];Prompt [txt];Answer [txt];Match [bool];Notes [txt]
#1;719;telefon;telefon;YES;
#2;556;gwizdek;gwizdek;YES;
#3;1246;kroki;kroki;YES;
python wav2time.py data/efekty-11_24_1033.wav
Adjust tags placement (rarely required) and prompt/answer codes.
Export audited labels to a text file.
python time2csv.py data/etykiety-out-11_24_1033.txtThe result is a CSV file ready for opening in other software and analysis.
In practical tests scripts save over 50% of time required to produce the labels suitable for calculating the required delays. The whole fun is the step which does speech recognition as it could potentially automate the process nearly in full once the emission log is available.
Accurate log of prompts' emissions could automate nearly everything. Most of the time in the workflow outlined above is consumed on entering names of the effects. Simple look-up table of responses ("kroki": "chodzenie", "obcasy", ...) could allow the system to skip prompts' identification workflow step and match responses to prompts reliably without any human intervention.