DCASE 2020: Sound event localization and detection (SELD) task

Please visit the official webpage of the DCASE 2020 Challenge for details missing in this repo.

As the baseline method for the SELD task, we use the SELDnet method studied in the following papers. If you are using this baseline method or the datasets in any format, then please consider citing the following two papers. If you want to read more about generic approaches to SELD then check here.

Sharath Adavanne, Archontis Politis, Joonas Nikunen and Tuomas Virtanen, "Sound event localization and detection of overlapping sources using convolutional recurrent neural network" in IEEE Journal of Selected Topics in Signal Processing (JSTSP 2018)

Sharath Adavanne, Archontis Politis and Tuomas Virtanen, "Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network" in the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019)

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, sound event detection (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, direction of arrival (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.

The figure below visualizes the SELDnet input and outputs for one of the recordings in the dataset. The horizontal-axis of all sub-plots for a given dataset represents the same time frames, the vertical-axis for spectrogram sub-plot represents the frequency bins, vertical-axis for SED reference and prediction sub-plots represents the unique sound event class identifier, and for the DOA reference and prediction sub-plots, it represents the distances along the Cartesian axes. The figures represents each sound event class and its associated DOA outputs with a unique color. Similar plot can be visualized on your results using the provided script.

DATASETS

The participants can choose either of the two or both the following datasets,

• TAU-NIGENS Spatial Sound Events 2020 - Ambisonic
• TAU-NIGENS Spatial Sound Events 2020 - Microphone Array

These datasets contain recordings from an identical scene, with TAU-NIGENS Spatial Sound Events 2020 - Ambisonic providing four-channel First-Order Ambisonic (FOA) recordings while TAU-NIGENS Spatial Sound Events 2020 - Microphone Array provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array, and additional information on the spatial characteristics of each format can be found below. The participants can choose one of the two, or both the datasets based on the audio format they prefer. Both the datasets, consists of a development and evaluation set.

The development set consists of 600, one minute long recordings sampled at 24000 Hz. All participants are expected to use the fixed splits provided in the baseline method for reporting the development scores. 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 is released during the evaluation phase of the challenge. After the end of the DCASE2020 Challenge, the unknown labels of the evaluation set are also released for further testing and comparison of methods out of the challenge, with the challenge results.

More details on the recording procedure and dataset can be read on the DCASE 2020 task webpage, and on the following report:

Archontis Politis, Sharath Adavanne, and Tuomas Virtanen (2020). "A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection". In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE2020), Tokyo, Japan

A longer version of the report with additional details can be also found in arXiv.

The development and evaluation datasets in both formats can be downloaded from the link

TAU-NIGENS Spatial Sound Events 2020 - Ambisonic and Microphone Array, Development & Evaluation 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.
• The calculate_dev_results_from_dcase_output.py script computes the metrics results on your DCASE output format files. You can switch between using polar or Cartesian based scoring. Ideally both should give identical results.

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 default parameters using

python3 seld.py

• Additionally, you can add/change parameters by using a unique identifier <task-id> in if-else loop as seen in the parameter.py script and call them as following

python3 seld.py <task-id> <job-id>

Where <job-id> is a unique identifier which is used for output filenames (models, training plots). You can use any number or string for this.

In order to get baseline results on the development set for Microphone array recordings, you can run the following command

python3 seld.py 2

Similarly, for Ambisonic format baseline results, run the following command

python3 seld.py 4

• By default, the code runs in quick_test = True mode. This trains the network for 2 epochs on only 2 mini-batches. Once you get to run the code sucessfully, set quick_test = False in parameter.py script and train on the entire data.

• 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

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 two metrics are more focused on the detection part, also referred as the location-aware detection, correposnding to the error rate (ER_20°) and F-score (F_20°) 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 next two metrics are more focused on the localization part, also referred as the class-aware localization, corresponding to the localization error (LE_CD) in degrees, and a localization Recall (LR_CD) in one-second non-overlapping segments, where the subscript refers to classification-dependent. 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

Dataset	ER20°	F20°	LECD	LRCD
Ambisonic (FOA)	0.72	37.4 %	22.8°	60.7 %
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

• Before submission, make sure your SELD results look good by visualizing the results using visualize_SELD_output.py script
• Make sure the file-wise output you are submitting is produced at 100 ms hop length. At this hop length a 60 s audio file has 600 frames.

For more information on the submission file formats check the website

License

Except for the contents in the metrics folder that have MIT License. The rest of the repository is licensed under the TAU License.

Acknowledgments

The research leading to these results has received funding from the European Research Council under the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.