Communications of the ACM

housed in an aluminum casing we chose to reduce RFI interference and solar heat gain. The microphone module is mounted externally via a flexible metal gooseneck attachment, making it possible to reconfigure the sensor node for deployment in varying locations, including sides of buildings, light poles, and building ledges. Apart from continuous SPL measurements, we designed the nodes to sample 10-second audio snippets at random intervals over a limited period of time, collecting data to train and benchmark our machine-listening solutions. SONYC compresses the audio using the lossless FLAC audio coding format, using 4,096-bit AES encryption and the RSA public/pri-vate key-pair encryption algorithm. Sensor nodes communicate with the server via a virtual private network, uploading audio and SPL data at one-minute intervals.

As of December 2018, the parts of each sensor cost approximately $80 using mostly off-the-shelf components. We fully expect to reduce the unit cost significantly through custom redesign for high-volume, third-party assembly. However, even at the current price, SONYC sensors are significantly more affordable, and thus amenable to large-scale deployment, than existing noise-monitoring solutions. Moreover, this reduced cost does not come at the expense of measurement accuracy, with our sensors’ performance comparable to high-quality devices that are orders of magnitude more costly while outperforming solutions in the same price range. Finally, the dedicated computing core opens the possibility for edge computing, particularly for in-situ machine listening intended to automatically and robustly identify the presence of common sound sources. This unique feature of SONYC goes well beyond the capabilities of existing noise-monitoring solutions.

Machine Listening at the Edge

Machine listening is the auditory coun-
terpart to computer vision, combining
techniques from signal processing and
machine learning to develop systems
able to extract meaningful information
from sound. In the context of SONYC,
we focus on developing computational
methods to automatically detect specif-
ic types of sound sources (such as jack-
hammers, idling engines, car horns,
and police sirens) from environmental
audio. Detection is a challenge, given
the complexity and diversity of sources,
auditory scenes, and background con-
ditions routinely found in noisy urban
acoustic environments.

We thus created an urban sound taxonomy, annotated datasets, and various cutting-edge methods for urban sound-source identification. 25, 26 Our research shows that feature learning, using even simple dictionary-based methods (such as spherical k-means) makes for significant improvement in performance over the traditional approach of feature engineering. Moreover, we have found that temporal-shift invariance, whether through modulation spectra or deep convolutional networks, is crucial not only for overall accuracy but also to increase robustness in low signal-to-noise-ra-tio (SNR) conditions, as when sources of interest are in the background of acoustic scenes. Shift invariance also results in more compact machines that can be trained with less data, thus adding greater value for edge-computing solutions. More recent results highlight the benefits of using convolutional recurrent architectures, as well as ensembles of various models via late fusion.

Deep-learning models necessitate
large volumes of labeled data tradi-
tionally unavailable for environmental
sound. Addressing this lack of data, we
have developed an audio data augmen-
tation framework that systematically
deforms the data using well-known
audio transformations (such as time
stretching, pitch shifting, dynamic
range compression, and addition of
background noise at different SNRs),
significantly increasing the amount of
data available for model training. We
also developed an open source tool
for soundscape synthesis. 27 Given a
collection of isolated sound events,
it functions as a high-level sequencer
that can generate multiple sound-
scapes from a single probabilistically
defined “specification.” We generated
large datasets of perfectly annotated
data in order to assess algorithmic
performance as a function of, say,
maximum polyphony and SNR ratio,
safety, traffic, and taxi activity to con-
struction, making much of it publicly
available.c Our work involves close
collaboration with city agencies, in-
cluding DEP, DOHMH, various busi-
ness improvement districts, and
private initiatives (such as LinkNYC)
that provide access to existing infra-
structure. As a powerful sensing-and-
analysis infrastructure, SONYC thus
holds the potential to empower new
research in environmental psychol-
ogy, public health, and public policy,
as well as empower citizens seeking
to improve their own communities.
We next describe the technology and
methods underpinning the project,
presenting some of our early findings
and future challenges.

Acoustic Sensor Network

As mentioned earlier, SONYC’s intelligent sensing platform should be scalable and capable of source identification and high-quality, round-the-clock noise monitoring. To that end we have developed an acoustic sensor18 (see Figure 2) based on the popular Raspberry Pi single-board computer outfitted with a custom microelectromechanical systems (MEMS) microphone module. We chose MEMS microphones for their low cost and consistency across units and size, which can be 10x smaller than conventional microphones. Our custom standalone microphone module includes additional circuitry, including in-house analog-to-digital converters and pre-amp stages, as well as an on-board microcontroller that enables preprocessing of the incoming audio signal to compensate for the microphone’s frequency response. The digital MEMS microphone features a wide dynamic range of 32dBA–120dBA, ensuring all urban sound pressure levels are monitored effectively. We calibrated it using a precision-grade sound-level meter as reference under low-noise anechoic conditions and was empirically shown to produce sound-pressure-level data at an accuracy level compliant with the ANSI Type- 2 standard20 required by most local and national noise codes.