Data-driven decomposition of crowd noise from indoor sporting events.

Separating crowd responses from raw acoustic signals at sporting events is challenging because recordings contain complex combinations of acoustic sources, including crowd noise, music, individual voices, and public address (PA) systems. This paper presents a data-driven decomposition of recordings of 30 collegiate sporting events. The decomposition uses machine-learning methods to find three principal spectral shapes that separate various acoustic sources. First, the distributions of recorded one-half-second equivalent continuous sound levels from men's and women's basketball and volleyball games are analyzed with regard to crowd size and venue. Using 24 one-third-octave bands between 50 Hz and 10 kHz, spectrograms from each type of game are then analyzed. Based on principal component analysis, 87.5% of the spectral variation in the signals can be represented with three principal components, regardless of sport, venue, or crowd composition. Using the resulting three-dimensional component coefficient representation, a Gaussian mixture model clustering analysis finds nine different clusters. These clusters separate audibly distinct signals and represent various combinations of acoustic sources, including crowd noise, music, individual voices, and the PA system.

Toward a dynamic national transportation noise map: Modeling temporal variability of traffic volume.

The National Transportation Noise Map (NTNM) gives time-averaged traffic noise across the continental United States (CONUS) using annual average daily traffic. However, traffic noise varies significantly with time. This paper outlines the development and utility of a traffic volume model which is part of VROOM, the Vehicular Reduced-Order Observation-based model, which, using hourly traffic volume data from thousands of traffic monitoring stations across CONUS, predicts nationwide hourly varying traffic source noise. Fourier analysis finds daily, weekly, and yearly temporal traffic volume cycles at individual traffic monitoring stations. Then, principal component analysis uses denoised Fourier spectra to find the most widespread cyclic traffic patterns. VROOM uses nine principal components to represent hourly traffic characteristics for any location, encapsulating daily, weekly, and yearly variation. The principal component coefficients are predicted across CONUS using location-specific features. Expected traffic volume model sound level errors-obtained by comparing predicted traffic counts to measured traffic counts-and expected NTNM-like errors, are presented. VROOM errors are typically within a couple of decibels, whereas NTNM-like errors are often inaccurate, even exceeding 10 decibels. This work details the first steps towards creation of a temporally and spectrally variable national transportation noise map.

A coherence-based phase and amplitude gradient estimator method for calculating active acoustic intensity.

The phase and amplitude gradient estimator (PAGE) method [Thomas, Christensen, and Gee, J. Acoust. Soc. Am. 137, 3366-3376 (2015)] has been developed as an alternative to the traditional p-p method for calculating energy-based acoustic measures such as active acoustic intensity. While this method shows many marked improvements over the traditional method, such as a wider valid frequency bandwidth for broadband sources, contaminating noise can lead to inaccurate results. Contaminating noise degrades performance for both the traditional and PAGE methods and causes probe microphone pairs to exhibit low coherence. When coherence is low, better estimates of the pressure magnitude and gradient can be obtained by using a coherence-based approach, which yields a more accurate intensity estimate. This coherence-based approach to the PAGE method, known as the CPAGE method, employs two main coherence-based adjustments. The pressure magnitude adjustment mitigates the negative impact of uncorrelated contaminating noise and improves intensity magnitude calculation. The phase gradient adjustment uses coherence as a weighting to calculate the phase gradient for the probe and improves primarily the calculation of intensity direction. Though requiring a greater computation time than the PAGE method, the CPAGE method is shown to improve intensity calculations, both in magnitude and direction.

Automatic classification and reduction of wind noise in spectral data.

Outdoor acoustic data often include non-acoustic pressures caused by atmospheric turbulence, particularly below a few hundred Hz in frequency, even when using microphone windscreens. This paper describes a method for automatic wind-noise classification and reduction in spectral data without requiring measured wind speeds. The method finds individual frequency bands matching the characteristic decreasing spectral slope of wind noise. Uncontaminated data from several short-timescale spectra can be used to obtain a decontaminated long-timescale spectrum. This method is validated with field-test data and can be applied to large datasets to efficiently find and reduce the negative impact of wind noise contamination.

Bandwidth extension of narrowband-signal intensity calculation using additive, low-level broadband noise.

Calculation of acoustic intensity using the phase and amplitude gradient estimator (PAGE) method has been shown to increase the effective upper frequency limit beyond the traditional p-p method when the source of interest is broadband in frequency [Torrie, Whiting, Gee, Neilsen, and Sommerfeldt, Proc. Mtgs. Acoust. 23, 030005 (2015)]. PAGE processing calculates intensity for narrowband sources without bias error up to the spatial Nyquist frequency [Succo, Sommerfeldt, Gee, and Neilsen, Proc. Mtgs. Acoust. 30, 030015 (2018)]. The present work demonstrates that for narrowband sources with frequency content above the spatial Nyquist frequency, additive low-level broadband noise can improve intensity calculations. To be effective, the angular separation between the source and additive noise source should be less than 30°, while using phase unwrapping with a smaller angular separation will increase the usable bandwidth. The upper frequency limit for the bandwidth extension depends on angular separation, sound speed, and probe microphone spacing. Assuming the signal-to-additive-noise ratio (SNRa) is larger than 10 dB, the maximum level and angular bias errors incurred by the additive broadband noise beneath the frequency limit-or up until probe scattering effects must be taken into account-are less than 0.5 dB and 2.5°, respectively. Smaller angular separation yields smaller bias errors.

The effects of contaminating noise on the calculation of active acoustic intensity for pressure gradient methods.

Bias errors for two-dimensional active acoustic intensity using multi-microphone probes have been previously calculated for both the traditional cross-spectral and the Phase and Amplitude Gradient Estimator (PAGE) methods [Whiting, Lawrence, Gee, Neilsen, and Sommerfeldt, J. Acoust. Soc. Am. 142, 2208-2218 (2017)]. Here, these calculations are expanded to include errors due to contaminating noise, as well as probe orientation. The noise can either be uncorrelated at each microphone location or self-correlated; the self-correlated noise is modeled as a plane-wave with a varying angle of incidence. The intensity errors in both magnitude and direction are dependent on the signal-to-noise ratio (SNR), frequency, source properties, incidence angles, probe configuration, and processing method. The PAGE method is generally found to give more accurate results, especially in direction; however, uncorrelated noise with a low SNR (below 10-15 dB) and low frequency (wavelengths more than 1/4 the microphone spacing) can yield larger errors in magnitude than the traditional method-though a correction for this is possible. Additionally, contaminating noise does not necessarily impact the possibility of using the PAGE method for broadband signals beyond a probe's spatial Nyquist frequency.