Hirotaka Nakasone

Variations in recorded acoustic gunshot waveforms generated by small firearms

Analysis of recorded acoustic gunshot signals to determine firearm waveform characteristics requires an understanding of the impulsive signal events, how the waveforms vary among different sources, and how the waveforms are affected by the environment and the recording system. This paper presents empirical results from waveforms produced by different small firearms and an analysis of their variations under different and controlled conditions. Acoustic signals were generated using multiple firearm makes and models firing different ammunition types. Simultaneous recordings from the microphones located at different distances from the source and at different azimuth angles (from the line-of-fire) were used to study source characteristics and sound propagation effects. The results indicate that recorded gunshot waveforms generally consist of multiple acoustic events, and these are observable depending on the received distance and azimuth angle. The source blast size, microphone distance, and microphone azimuth angle are the primary factors affecting the recorded muzzle blast characteristics. Ground or object reflections and ballistic shockwaves and their reflections can interfere with the muzzle blast waveform and its measurements. This experiment confirmed and quantified the wide range of correlation results between waveforms recorded from different source, microphone distance, and microphone angle configurations.

An introduction to forensic gunshot acoustics

Due to the proliferation of audio recording devices in the military, law enforcement, and the civilian community, there has been an increase in the number of recorded gunshot sounds submitted for forensic analysis. A gunshot sound is composed of one or more discrete acoustic transient events. The two primary acoustic events are the muzzle blast (bang) and the ballistic shockwave (crack). The acoustic event characteristics depend on their source generating mechanisms and vary according to the firearm make, model, barrel length, and the specific ammunition characteristics. Forensic gunshot analysis deals with a single recorded shot lasting for a fraction of a second. These acoustic events are usually high intensity, often up to 160 dB SPL, are highly directional, and are often recorded in high distortion environments. Forensic gunshot analysis must take into account variations in the source generation characteristics and the sources of distortion for these recorded acoustic events in order to answer these f...

Speaker Recognition Score-Normalization to Compensate for SNR and Duration

The decision criterion for automatic speaker verification tests is based on minimization of a weighted sum of the miss and false alarm probabilities. These probabilities are derived from an evaluation of claimant and impostor scores using a representative population of recorded speech samples. However, in applications such as forensic speaker verification, the signal quality and the recording conditions of the speech samples are usually unknown and generally not matched to the evaluation conditions for the defined error probabilities. For example, test samples are often of short duration, have significant noise, and are from uncertain channels. It is therefore necessary to normalize the speaker test scores or to adjust detection thresholds in accordance with the recorded signal conditions. Instead of accounting for all possibilities, evaluations were conducted for a few specific joint combinations of signal-to-noise ratio (SNR) and speech duration for both the training and test sets. A composite regression model was developed to predict the necessary adjustments for any measured value of these conditions. In addition, a method is discussed to interpret the normalized scores relative to a set of desired Type I and Type II error probabilities

Progress toward a forensic voice data format standard

The de facto international standard for the forensic exchange of data for biometric recognition is ANSI/NIST ITL-1/2, “Data Format for the Interchange of Fingerprint, Facial, and Other Biometric Information.” This format is used by law enforcement, intelligence, military, and homeland security organizations thoughout the world to exchange fingerprint, face, scar/mark/tatoo, iris, and palmprint data. To date, however, there is no provision within the standard for the exchange of audio data for the purpose of forensic speaker recognition. During the recent 5-year update process for ANSI/NIST ITL-1/2, a consensus decision was made to advance a voice data format type under the name “Type 11 record.” Creating such an exchange format type, however, is far from straight forward—the problem being not the encoding of the autio data, for which many accepted standards exist, but rather in reaching a consensus on the metadata needed to support the varied mission requirements across the stakeholder communities. In thi...