DCASE 2020: SELD using squeeze-excitation residual networks

Please visit the official webpage of the DCASE 2020 Challenge for comparison with other submissions.

The main objective of this submission was to study how squeeze-excitation techniques can improve the behavior of sound event detection and localization (SELD) systems. To do so, we start from the network presented as a baseline consisting of a CRNN and replace the convolutional layers by Conv-StandardPOST blocks. This block was presented in:

Naranjo-Alcazar, J., Perez-Castanos, S., Zuccarello, P., & Cobos, M. (2020). Acoustic Scene Classification with Squeeze-Excitation Residual Networks. IEEE Access.

This repo implementation is presented in:

Naranjo-Alcazar, Javier, et al. "Sound Event Localization and Detection using Squeeze-Excitation Residual CNNs." arXiv preprint arXiv:2006.14436 (2020).

Please consider citing these works if the code or something presented in them has been used.

BASELINE METHOD

In comparison to the SELDnet studied in the papers above, we have changed the following to improve its performance and evaluate the performance better.

• Features: The original SELDnet employed naive phase and magnitude components of the spectrogram as the input feature for all input formats of audio. In this baseline method, we use separate features for first-order Ambisonic (FOA) and microphone array (MIC) datasets. As the interaural level difference feature, we employ the 64-band mel energies extracted from each channel of the input audio for both FOA and MIC. To encode the interaural time difference features, we employ intensity vector features for FOA, and generalized cross correlation features for MIC.
• Loss/Objective: The original SELDnet employed mean square error (MSE) for the DOA loss estimation, and this was computed irrespecitve of the presence or absence of the sound event. In the current baseline, we used a masked-MSE, which computes MSE only when the sound event is active in the reference.
• Evaluation metrics: The performance of the original SELDnet was evaluated with stand-alone metrics for detection, and localization. Mainly because there was no suitable metric which could jointly evaluate the performance of localization and detection. Since then, we have proposed a new metric that can jointly evaluate the performance (more about it is described in the metrics section below), and we employ this new metric for evaluation here.

The final SELDnet architecture is as shown below. The input is the multichannel audio, from which the different acoustic features are extracted based on the input format of the audio. Based on the chosen dataset (FOA or MIC), the baseline method takes a sequence of consecutive feature-frames and predicts all the active sound event classes for each of the input frame along with their respective spatial location, producing the temporal activity and DOA trajectory for each sound event class. In particular, a convolutional recurrent neural network (CRNN) is used to map the frame sequence to the two outputs in parallel. At the first output, SED is performed as a multi-label multi-class classification task, allowing the network to simultaneously estimate the presence of multiple sound events for each frame. At the second output, DOA estimates in the continuous 3D space are obtained as a multi-output regression task, where each sound event class is associated with three regressors that estimate the Cartesian coordinates x, y and z axes of the DOA on a unit sphere around the microphone.

The SED output of the network is in the continuous range of [0 1] for each sound event in the dataset, and this value is thresholded to obtain a binary decision for the respective sound event activity. Finally, the respective DOA estimates for these active sound event classes provide their spatial locations.

SUBMISSION MODIFICATION

This image shows the submission architecture:

DATASET

The dataset used has been:

• TAU-NIGENS Spatial Sound Events 2020 - Microphone Array

TAU-NIGENS Spatial Sound Events 2020 - Microphone Array provides four-channel directional microphone recordings from a tetrahedral array configuration. This format is extracted from the same microphone array, and additional information on the spatial characteristics of each format can be found below. This dataset consists of a development and evaluation set. The development set consists of 600, one minute long recordings sampled at 24000 Hz. We use 400 recordings for training split (fold 3 to 6), 100 for validation (fold 2) and 100 for testing (fold 1). The evaluation set consists of 200, one-minute recordings, and will be released at a later point.

More details on the recording procedure and dataset can be read on the DCASE 2020 task webpage.

The two development datasets can be downloaded from the link - TAU-NIGENS Spatial Sound Events 2020 - Ambisonic and Microphone Array, Development dataset

Getting Started

This repository consists of multiple Python scripts forming one big architecture used to train the SELDnet.

• The batch_feature_extraction.py is a standalone wrapper script, that extracts the features, labels, and normalizes the training and test split features for a given dataset. Make sure you update the location of the downloaded datasets before.
• The parameter.py script consists of all the training, model, and feature parameters. If a user has to change some parameters, they have to create a sub-task with unique id here. Check code for examples.
• The cls_feature_class.py script has routines for labels creation, features extraction and normalization.
• The cls_data_generator.py script provides feature + label data in generator mode for training.
• The keras_model.py script implements the SELDnet architecture.
• The evaluation_metrics.py script implements the core metrics from sound event detection evaluation module http://tut-arg.github.io/sed_eval/ and the DOA metrics explained in the paper. These were used in the DCASE 2019 SELD task. We use this here to just for legacy comparison
• The SELD_evaluation_metrics.py script implements the metrics for joint evaluation of detection and localization.
• The seld.py is a wrapper script that trains the SELDnet. The training stops when the SELD error (check paper) stops improving.

Additionally, we also provide supporting scripts that help analyse the results.

• visualize_SELD_output.py script to visualize the SELDnet output

Prerequisites

The provided codebase has been tested on python 3.6.9/3.7.3 and Keras 2.2.4/2.3.1

Training the SELDnet

In order to quickly train SELDnet follow the steps below.

• For the chosen dataset (Ambisonic or Microphone), download the respective zip file. This contains both the audio files and the respective metadata. Unzip the files under the same 'base_folder/', ie, if you are Ambisonic dataset, then the 'base_folder/' should have two folders - 'foa_dev/' and 'metadata_dev/' after unzipping.

• Now update the respective dataset name and its path in parameter.py script. For the above example, you will change dataset='foa' and dataset_dir='base_folder/'. Also provide a directory path feat_label_dir in the same parameter.py script where all the features and labels will be dumped.

• Extract features from the downloaded dataset by running the batch_feature_extraction.py script. Run the script as shown below. This will dump the normalized features and labels in the feat_label_dir folder.

python3 batch_feature_extraction.py

You can now train the SELDnet using this subimssion modifications. Parameters that MUST be indicated are --baseline and --ratio

python3 seld.py --baseline False --ratio 4

executes ConvStandard modules with ratio =4. If you want to execute the baseline code, set --baseline to True. If want to execute residual learning without squeeze-excitation:

python3 seld.py --baseline False --ratio 0

• By default, the code runs in quick_test = False mode. Setting quick_test = True in parameter.py trains the network for 2 epochs on only 2 mini-batches.

• The code also plots training curves, intermediate results and saves models in the model_dir path provided by the user in parameter.py file.

• In order to visualize the output of SELDnet and for submission of results, set dcase_output=True and provide dcase_dir directory. This will dump file-wise results in the directory, which can be individually visualized using visualize_SELD_output.py script.

Results on development dataset (baseline)

As the evaluation metrics we use two different approaches as discussed in our recent paper below

Annamaria Mesaros, Sharath Adavanne, Archontis Politis, Toni Heittola, and Tuomas Virtanen. Joint measurement of localization and detection of sound events. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). New Paltz, NY, Oct 2019.

The first metric is more focused on the detection part, also referred as the location-aware detection, which gives us the error rate (ER) and F-score (F) in one-second non-overlapping segments. We consider the prediction to be correct if the prediction and reference class are the same, and the distance between them is below 20°. The second metric is more focused on the localization part, also referred as the class-aware localization, which gives us the DOA error (DE), and F-score (DE_F) in one-second non-overlapping segments. Unlike the location-aware detection, we do not use any distance threshold, but estimate the distance between the correct prediction and reference.

The evaluation metric scores for the test split of the development dataset is given below

Baseline results

Dataset	ER	F	DE	DE_F
Microphone Array (MIC)	0.78	31.4 %	27.3°	59.0 %

Note: The reported baseline system performance is not exactly reproducible due to varying setups. However, you should be able to obtain very similar results.

Submission results

Development stage (results on testing folder)

set mode to dev

ratio	ER	F	DE	DE_F
0	0.68	42.3	22.5	65.1
1	0.70	39.2	23.5	63.6
2	0.69	40.4	23.2	62.1
4	0.68	40.9	23.3	65.0
8	0.69	40.8	23.5	63.8
16	0.69	40.7	23.3	62.8

Challenge results

• The team submission ranked 11/15

• Best system ranked, ratio = 1, 30/43

ratio	ER	F	DE	DE_F
organization baseline	0.70	39.5	23.2	62.1
0	0.61	48.3	19.2	65.9
1	0.61	49.1	19.5	67.1
8	0.64	46.7	20.0	64.5
16	0.63	47.3	19.5	65.5