Sound Source Localization Eval

sound-source-localization-eval

Localizing Visual Sounds the Hard Way — Honglie Chen et al. (arXiv:2104.02691, 2021)

What this evaluates

This evaluation probes a model's ability to spatially localize sound sources in images or video frames given an accompanying audio clip. It measures how accurately the predicted bounding box overlaps with ground-truth annotations provided by multiple human annotators.

Datasets

• Flickr SoundNet Testset — total 250; splits: test (250)
• VGG-Sound Source (VGG-SS) — total ?; splits: test (-1)

Metrics

• cIoU (primary) — range: [0, 1]
  • Consensus Intersection over Union (cIoU) measures the average overlap between predicted bounding boxes and consensus ground-truth boxes across multiple annotators.
• AUC — range: [0, 1]
  • Area Under Curve (AUC) summarizes localization performance across varying IoU thresholds or confidence scores.

Input / output format

Input: A single image frame (224×224×3 tensor) paired with a 20-second audio clip (257×300 magnitude spectrogram).

Output: Bounding box coordinates predicting the sound source location in the image.

Scoring recipe

def compute_ciou(predictions, gold_boxes):
    total_ciou = 0.0
    for pred_box, annotator_boxes in zip(predictions, gold_boxes):
        ious = [calculate_iou(pred_box, g_box) for g_box in annotator_boxes]
        total_ciou += np.mean(ious)
    return total_ciou / len(predictions)

Common pitfalls

• Training uses only center frames, but testing feeds the full-length audio spectrogram into the network.
• The test set size is fixed at 250 pairs, distinct from the variable training subsets (10k/144k).
• AUC implementation is not specified; readers must verify whether it uses IoU-threshold or confidence-score curves.

Evidence (verbatim from paper)

In order to quantitatively evaluate the proposed approach, we adopt the evaluation metrics used in [27, 31]: Consensus Intersection over Union (cIoU) and Area Under Curve (AUC) are reported for each model on two test sets, as detailed next.

Citation

@misc{chen2021localizingvisualsounds,
  title={Localizing Visual Sounds the Hard Way},
  author={Honglie Chen et al.},
  year={2021},
  note={arXiv:2104.02691}
}

• arXiv: 2104.02691