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2. Stenomask - Wikipedia

   en.wikipedia.org/wiki/Stenomask

   The purpose of a stenomask is to allow a person to speak without being heard by other people, and to keep background noise away from the microphone. A stenomask is useful for speech recognition applications, because it allows voice transcription in noisy environments. Perhaps more importantly, a stenomask silences the user's voice so that it ...

3. Krisp - Wikipedia

   en.wikipedia.org/wiki/Krisp

   Krisp's main product is a software application that can remove background noises and voices from audio in real-time. The software uses machine learning algorithms to analyze the audio signal and separate the speech from background noise, allowing the speech to be output in clear, noise-free audio. This technology has a wide range of ...

4. Electronic voice phenomenon - Wikipedia

   en.wikipedia.org/wiki/Electronic_voice_phenomenon

   Capture errors are anomalies created by the method used to capture audio signals, such as noise generated through the over-amplification of a signal at the point of recording. [ 39 ] [ 55 ] Artifacts created during attempts to boost the clarity of an existing recording might explain some EVP.

5. Audio forensics - Wikipedia

   en.wikipedia.org/wiki/Audio_forensics

   Audio enhancement is a forensic process that aims at improving the audio file intelligibility by removing and cleaning unwanted noise from an otherwise unintelligible recording. [ 2 ] The forensic scientists try to remove these noises without affecting the original information present in the audio file.

6. Comparison of analog and digital recording - Wikipedia

   en.wikipedia.org/wiki/Comparison_of_analog_and...

   For electronic audio signals, sources of noise include mechanical, electrical and thermal noise in the recording and playback cycle. The amount of noise that a piece of audio equipment adds to the original signal can be quantified. Mathematically, this can be expressed by means of the signal-to-noise ratio (SNR or S/N ratio). Sometimes the ...

7. Voice activity detection - Wikipedia

   en.wikipedia.org/wiki/Voice_activity_detection

   It uses inverse filtering trained on non-speech segments to filter out background noise, so that it can then more reliably use a simple power-threshold to decide if a voice is present. [ 5 ] The G.729 standard calculates the following features for its VAD: line spectral frequencies , full-band energy, low-band energy (<1 kHz), and zero-crossing ...