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Audio processing in police investigations

Profile image of David LuknowskyDavid Luknowsky

2004, Canadian Acoustics

Abstract
sparkles

AI

The paper focuses on the audio processing techniques utilized by the Audio and Video Analysis Section of the Royal Canadian Mounted Police (RCMP) to enhance intelligibility of audio recordings from various sources. It discusses common types of interference, such as hum and tones, and the methods employed to mitigate these disturbing elements using filters including comb filters and spectral inverse filters. The key emphasis is on achieving optimal voice intelligibility rather than merely producing aesthetically pleasing recordings.

FAQs

sparkles

AI

What techniques are effective for reducing hum and tonal noises in recordings?add

The study finds that comb filters effectively remove 60 Hz hum and harmonics, while adaptive filters tackle variable tones and predictable noise patterns, enhancing intelligibility in recordings.

How can audio filters improve intelligibility of low-quality recordings?add

Adaptive filters and spectral inverse filters can enhance the clarity of voices by boosting high-frequency components, thus converting muffled audio into clearer, more intelligible speech.

What common challenges arise when processing police audio recordings?add

Investigators frequently encounter issues with varying voice levels and unwanted interferences from electronics, necessitating complex adjustments to maintain clarity and intelligibility in recordings.

Why is maintaining high-frequency information crucial in forensic audio processing?add

Retaining high frequencies, despite contributing to noise, is essential for preserving critical intelligibility cues, as voice clarity hinges on these frequencies, especially in murky audio environments.

How does digital audio editing assist in managing clipping artifacts in recordings?add

Digital audio editing enables precise waveform reconstruction to eliminate clipping, significantly enhancing the audio quality by interpolating between surrounding data points.

Figures (3)
One of the most common types of interference, and one of the simplest to reduce, is tones and hum. Hum typically comes from power lines, fluorescent lights or other electrical sources. It is seen in the spectrum as spikes at 60 Hz and harmonics thereof (Figure 1).
One of the most common types of interference, and one of the simplest to reduce, is tones and hum. Hum typically comes from power lines, fluorescent lights or other electrical sources. It is seen in the spectrum as spikes at 60 Hz and harmonics thereof (Figure 1).
Figure 2. Spectrum from figure 1 with hum reduced using a comb filter.
Figure 2. Spectrum from figure 1 with hum reduced using a comb filter.
Figure 3. Audio waveform with clipping (top) and corrected (bottom). Total length of audio shown is 9.6 ms.
Figure 3. Audio waveform with clipping (top) and corrected (bottom). Total length of audio shown is 9.6 ms.

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References (2)

  1. Paul, J.E., "Adaptive Digital Techniques For Audio Noise Cancellation," IEEE Signal Processing Magazine, Vol. 1, No. 4, pp. 2-7, 1979.
  2. J. L. Flanagan, Speech Analysis Synthesis and Perception, New York: Springer-Verlag, 1972, p. 163.

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Related topics

  • Computer Science
  • Algorithms
  • Acoustics
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  • Information Analysis
  • Radio Interference
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