Frequency Tracking Features for Data-Efficient Deep Siren Identification

The identification of siren sounds in urban soundscapes is a crucial safety aspect for smart vehicles and has been widely addressed by means of neural networks that ensure robustness to both the diversity of siren signals and the strong and unstructured background noise characterizing traffic. Convolutional neural networks analyzing spectrogram features of incoming signals achieve state-of-the-art performance when enough training data capturing the diversity of the target acoustic scenes is available. In practice, data is usually limited and algorithms should be robust to adapt to unseen acoustic conditions without requiring extensive datasets for re-training. In this work, given the harmonic nature of siren signals, characterized by a periodically evolving fundamental frequency, we propose a low-complexity feature extraction method based on frequency tracking using a single-parameter adaptive notch filter. The features are then used to design a small-scale convolutional network suitable for training with limited data. The evaluation results indicate that the proposed model consistently outperforms the traditional spectrogram-based model when limited training data is available, achieves better cross-domain generalization and has a smaller size.

Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?

In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not been thoroughly investigated in the audio domain, likely due to real-world data scarcity. In this work, we propose a novel approach to acoustic vehicle counting by developing: i) a traffic noise simulation framework to synthesize realistic vehicle pass-by events; ii) a strategy to mix synthetic and real data to train a deep-learning model for traffic counting. The proposed system is capable of simultaneously counting cars and commercial vehicles driving on a two-lane road, and identifying their direction of travel under moderate traffic density conditions. With only 24 hours of labeled real-world traffic noise, we are able to improve counting accuracy on real-world data from $63\%$ to $88\%$ for cars and from $86\%$ to $94\%$ for commercial vehicles.

Real-Time Acoustic Perception for Automotive Applications

In recent years the automotive industry has been strongly promoting the development of smart cars, equipped with multi-modal sensors to gather information about the surroundings, in order to aid human drivers or make autonomous decisions. While the focus has mostly been on visual sensors, also acoustic events are crucial to detect situations that require a change in the driving behavior, such as a car honking, or the sirens of approaching emergency vehicles. In this paper, we summarize the results achieved so far in the Marie Sklodowska-Curie Actions (MSCA) European Industrial Doctorates (EID) project Intelligent Ultra Low-Power Signal Processing for Automotive (I-SPOT). On the algorithmic side, the I-SPOT Project aims to enable detecting, localizing and tracking environmental audio signals by jointly developing microphone array processing and deep learning techniques that specifically target automotive applications. Data generation software has been developed to cover the I-SPOT target scenarios and research challenges. This tool is currently being used to develop low-complexity deep learning techniques for emergency sound detection. On the hardware side, the goal impels workflows for hardware-algorithm co-design to ease the generation of architectures that are sufficiently flexible towards algorithmic evolutions without giving up on efficiency, as well as enable rapid feedback of hardware implications of algorithmic decision. This is pursued though a hierarchical workflow that breaks the hardware-algorithm design space into reasonable subsets, which has been tested for operator-level optimizations on state-of-the-art robust sound source localization for edge devices. Further, several open challenges towards an end-to-end system are clarified for the next stage of I-SPOT.