A changepoint prefilter for sound event detection in long-term bioacoustic recordings.

Long-term soundscape recordings are useful for a variety of applications, most notably in bioacoustics. However, the processing of such data is currently limited by the ability to efficiently and reliably detect the target sounds, which are often sparse and overshadowed by environmental noise. This paper proposes a sound detector based on changepoint theory applied to a wavelet representation of the sound. In contrast to existing methods, in this framework, theoretical analysis of the detector's performance and optimality for downstream applications can be made. The relevant statistical and algorithmic developments to support these claims are presented. The method is then tested on a real task of detecting two bird species in acoustic surveys. Compared to commonly used alternatives, the proposed method consistently produced a lower false alarm rate and improved the survey efficiency as measured by the precision of the inferred population size. Finally, it is demonstrated how the method can be combined with a simple classifier to detect cat sounds in domestic recordings, which is an example from the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 workshop. The resulting performance is comparable to the state-of-the-art deep learning models and requires much less training data.

Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast.

Autonomous recording units are now routinely used to monitor birdsong, starting to supplement and potentially replace human listening methods. However, to date there has been very little systematic comparison of human and machine detection ability. We present an experiment based on broadcast calls of nocturnal New Zealand birds in an area of natural forest. The soundscape was monitored by both novice and experienced humans performing a call count, and autonomous recording units. We match records of when calls were broadcast with detections by both humans and machines, and construct a hierarchical generalized linear model of the binary variable of correct detection or not, with a set of covariates about the call (distance, sound direction, relative altitude, and line of sight) and about the listener (age, experience, and gender). The results show that machines and humans have similar listening ability. Humans are more homogeneous in their recording of sounds, and this was not affected by their individual experience or characteristics. Humans were affected by trial and location, in particular one of the stations located in a small but deep valley. Despite recorders being affected significantly more than people by distance, altitude, and line of sight, their overall detection probability was higher. The specific location of recorders seems to be the most important factor determining what they record, and we suggest that for best results more than one recorder (or at least, microphone) is needed at each station to ensure all bird sounds of interest are captured.

The impact of environmental factors in birdsong acquisition using automated recorders.

The use of automatic acoustic recorders is becoming a principal method to survey birds in their natural habitats, as it is relatively noninvasive while still being informative. As with any other sound, birdsong degrades in amplitude, frequency, and temporal structure as it propagates to the recorder through the environment. Knowing how different birdsongs attenuate under different conditions is useful to, for example, develop protocols for deploying acoustic recorders and improve automated detection methods, an essential part of the research field that is becoming known as ecoacoustics. This article presents playback and recapture (record) experiments carried out under different environmental conditions using twenty bird calls from eleven New Zealand bird species in a native forest and an open area, answering five research questions: (1) How does birdsong attenuation differ between forest and open space? (2) What is the relationship between transmission height and birdsong attenuation? (3) How does frequency of birdsong impact the degradation of sound with distance? (4) Is birdsong attenuation different during the night compared to the day? and (5) what is the impact of wind on attenuation? Bird calls are complex sounds; therefore, we have chosen to use them rather than simple tones to ensure that this complexity is not missed in the analysis. The results demonstrate that birdsong transmission was significantly better in the forest than in the open site. During the night, the attenuation was at a minimum in both experimental sites. Transmission height affected the propagation of the songs of many species, particularly the flightless ones. The effect of wind was severe in the open site and attenuated lower frequencies. The reverberations due to reflective surfaces masked higher frequencies (8 kHz) in the forest even at moderate distances. The findings presented here can be applied to develop protocols for passive acoustic monitoring. Even though the attenuation can be generalized to frequency bands, the structure of the birdsong is also important. Selecting a reasonable sampling frequency avoids unnecessary data accumulation because higher frequencies attenuate more in the forest. Even at moderate distances, recorders capture significantly attenuated birdsong, and hence, automated analysis methods for field recordings need to be able to detect and recognize faint birdsong.

Birdsong Denoising Using Wavelets.

Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.