Soundscape Captioning using Sound Affective Quality Network and Large Language Model

We live in a rich and varied acoustic world, which is experienced by individuals or communities as a soundscape. Computational auditory scene analysis, disentangling acoustic scenes by detecting and classifying events, focuses on objective attributes of sounds, such as their category and temporal characteristics, ignoring the effect of sounds on people and failing to explore the relationship between sounds and the emotions they evoke within a context. To fill this gap and to automate soundscape analysis, which traditionally relies on labour-intensive subjective ratings and surveys, we propose the soundscape captioning (SoundSCap) task. SoundSCap generates context-aware soundscape descriptions by capturing the acoustic scene, event information, and the corresponding human affective qualities. To this end, we propose an automatic soundscape captioner (SoundSCaper) composed of an acoustic model, SoundAQnet, and a general large language model (LLM). SoundAQnet simultaneously models multi-scale information about acoustic scenes, events, and perceived affective qualities, while LLM generates soundscape captions by parsing the information captured by SoundAQnet to a common language. The soundscape caption's quality is assessed by a jury of 16 audio/soundscape experts. The average score (out of 5) of SoundSCaper-generated captions is lower than the score of captions generated by two soundscape experts by 0.21 and 0.25, respectively, on the evaluation set and the model-unknown mixed external dataset with varying lengths and acoustic properties, but the differences are not statistically significant. Overall, SoundSCaper-generated captions show promising performance compared to captions annotated by soundscape experts. The models' code, LLM scripts, human assessment data and instructions, and expert evaluation statistics are all publicly available.

AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyance

Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most previous research focused on psycho-acoustic quantities or automatic sound recognition. Few attempts were made to include appraisal (e.g., in circumplex frameworks). This paper proposes an artificial intelligence (AI)-based dual-branch convolutional neural network with cross-attention-based fusion (DCNN-CaF) to analyze automatic soundscape characterization, including sound recognition and appraisal. Using the DeLTA dataset containing human-annotated sound source labels and perceived annoyance, the DCNN-CaF is proposed to perform sound source classification (SSC) and human-perceived annoyance rating prediction (ARP). Experimental findings indicate that (1) the proposed DCNN-CaF using loudness and Mel features outperforms the DCNN-CaF using only one of them. (2) The proposed DCNN-CaF with cross-attention fusion outperforms other typical AI-based models and soundscape-related traditional machine learning methods on the SSC and ARP tasks. (3) Correlation analysis reveals that the relationship between sound sources and annoyance is similar for humans and the proposed AI-based DCNN-CaF model. (4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on sound-scape augmentation.

MirrorCalib: Utilizing Human Pose Information for Mirror-based Virtual Camera Calibration

In this paper, we present the novel task of estimating the extrinsic parameters of a virtual camera with respect to a real camera with one single fixed planar mirror. This task poses a significant challenge in cases where objects captured lack overlapping views from both real and mirrored cameras. To address this issue, prior knowledge of a human body and 2D joint locations are utilized to estimate the camera extrinsic parameters when a person is in front of a mirror. We devise a modified eight-point algorithm to obtain an initial estimation from 2D joint locations. The 2D joint locations are then refined subject to human body constraints. Finally, a RANSAC algorithm is employed to remove outliers by comparing their epipolar distances to a predetermined threshold. MirrorCalib is evaluated on both synthetic and real datasets and achieves a rotation error of 0.62{\deg}/1.82{\deg} and a translation error of 37.33/69.51 mm on the synthetic/real dataset, which outperforms the state-of-art method.

Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning

Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting audio events and scenes, but may ignore their perceptual quality that may impact humans' listening mood for the environment, e.g. annoyance. To this end, this paper proposes a novel hierarchical graph representation learning (HGRL) approach which links objective audio events (AE) with subjective annoyance ratings (AR) of the soundscape perceived by humans. The hierarchical graph consists of fine-grained event (fAE) embeddings with single-class event semantics, coarse-grained event (cAE) embeddings with multi-class event semantics, and AR embeddings. Experiments show the proposed HGRL successfully integrates AE with AR for AEC and ARP tasks, while coordinating the relations between cAE and fAE and further aligning the two different grains of AE information with the AR.