Post-Processing Independent Evaluation of Sound Event Detection Systems

Due to the high variation in the application requirements of sound event detection (SED) systems, it is not sufficient to evaluate systems only in a single operating mode. Therefore, the community recently adopted the polyphonic sound detection score (PSDS) as an evaluation metric, which is the normalized area under the PSD receiver operating characteristic (PSD-ROC). It summarizes the system performance over a range of operating modes resulting from varying the decision threshold that is used to translate the system output scores into a binary detection output. Hence, it provides a more complete picture of the overall system behavior and is less biased by specific threshold tuning. However, besides the decision threshold there is also the post-processing that can be changed to enter another operating mode. In this paper we propose the post-processing independent PSDS (piPSDS) as a generalization of the PSDS. Here, the post-processing independent PSD-ROC includes operating points from varying post-processings with varying decision thresholds. Thus, it summarizes even more operating modes of an SED system and allows for system comparison without the need of implementing a post-processing and without a bias due to different post-processings. While piPSDS can in principle combine different types of post-processing, we hear, as a first step, present median filter independent PSDS (miPSDS) results for this year's DCASE Challenge Task4a systems. Source code is publicly available in our sed_scores_eval package (https://github.com/fgnt/sed_scores_eval).Comment: submitted to DCASE Workshop 202

Threshold independent evaluation of sound event detection scores

International audiencePerforming an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detection (PSD)-receiver operating characteristic (ROC) and PSD score (PSDS) make an important step into the direction of an evaluation of SED systems which is independent from a certain decision threshold. This allows to obtain a more complete picture of the overall system behavior which is less biased by threshold tuning. Yet, the PSD-ROC is currently only approximated using a finite set of thresholds. The choice of the thresholds used in approximation, however, can have a severe impact on the resulting PSDS. In this paper we propose a method which allows for jointly computing system performance on an evaluation set for all possible thresholds jointly, enabling accurate computation not only of the PSD-ROC and PSDS but also of other collar-based and intersection-based performance curves. It further allows to select the threshold which best fulfills the requirements of a given application. Source code is made publicly available in our SED evaluation package sed scores eval 1

Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections

Funding Information: This Open Access publication was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491094227 “Open Access Publication Funding” and the University of Veterinary Medicine Hannover, Foundation.Peer reviewe

Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision

In this paper we present our audio tagging system for the DCASE 2019 Challenge Task 2. We propose a model consisting of a convolutional front end using log-mel-energies as input features, a recurrent neural network sequence encoder and a fully connected classifier network outputting an activity probability for each of the 80 considered event classes. Due to the recurrent neural network, which encodes a whole sequence into a single vector, our model is able to process sequences of varying lengths. The model is trained with only little manually labeled training data and a larger amount of automatically labeled web data, which hence suffers from label noise. To efficiently train the model with the provided data we use various data augmentation to prevent overfitting and improve generalization. Our best submitted system achieves a label-weighted label-ranking average precision (lwlrap) of 75.5% on the private test set which is an absolute improvement of 21.7% over the baseline. This system scored the second place in the teams ranking of the DCASE 2019 Challenge Task 2 and the fifth place in the Kaggle competition ``Freesound Audio Tagging 2019'' with more than 400 participants. After the challenge ended we further improved performance to 76.5% lwlrap setting a new state-of-the-art on this dataset.646

Post-Processing Independent Evaluation of Sound Event Detection Systems

International audienceDue to the high variation in the application requirements of sound event detection (SED) systems, it is not sufficient to evaluate systems only in a single operating point. Therefore, the community recently adopted the polyphonic sound detection score (PSDS) as an evaluation metric, which is the normalized area under the PSD-ROC. It summarizes the system performance over a range of operating points. Hence, it provides a more complete picture of the overall system behavior and is less biased by hyper parameter tuning. So far PSDS has only been computed over operating points resulting from varying the decision threshold that is used to translate the system output scores into a binary detection output. However, besides the decision threshold there is also the post-processing that can be changed to enter another operating mode. In this paper we propose the post-processing independent PSDS (piPSDS) which computes PSDS over operating points with varying post-processings and varying decision thresholds. It summarizes even more operating modes of an SED system and allows for system comparison without the need of implementing a post-processing and without a bias due to different post-processings. While piPSDS can in principle also combine different types of post-processing, we here, as a first step, present median filter independent PSDS (miPSDS) results for this year's DCASE Challenge Task4a systems. Source code is publicly available in our sed scores eval package 1

Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion

Gburrek T, Ebbers J, Häb-Umbach R, Wagner P. Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion. In: Proceedings of the 10 Speech Synthesis Workshop (SSW10). 2019.This paper presents an approach to voice conversion, which does neither require parallel data nor speaker or phone labels for training. It can convert between speakers which are not in the training set by employing the previously proposed concept of a factorized hierarchical variational autoencoder. Here, linguistic and speaker induced variations are separated upon the notion that content induced variations change at a much shorter time scale, i.e., at the segment level, than speaker induced variations, which vary at the longer utterance level. In this contribution we propose to employ convolutional instead of recurrent network layers in the encoder and decoder blocks, which is shown to achieve better phone recognition accuracy on the latent segment variables at frame-level due to their better temporal resolution. For voice conversion the mean of the utterance variables is replaced with the respective estimated mean of the target speaker. The resulting log-mel spectra of the decoder output are used as local conditions of a WaveNet which is utilized for synthesis of the speech waveforms. Experiments show both good disentanglement properties of the latent space variables, and good voice conversion performance, as assessed both quantitatively and qualitatively

Investigation into Target Speaking Rate Adaptation for Voice Conversion

Kuhlmann M, Seebauer FM, Ebbers J, Wagner P, Haeb-Umbach R. Investigation into Target Speaking Rate Adaptation for Voice Conversion. In: Proceedings of Interspeech. 2022: 4930--4934.Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a popular approach for voice conversion, which can be trained with non-parallel and unlabeled speech data. However, previous approaches perform disentanglement only implicitly via some sort of information bottleneck or normalization, where it is usually hard to find a good trade-off between voice conversion and content reconstruction. Further, previous works usually do not consider an adaptation of the speaking rate to the target speaker or they put some major restrictions to the data or use case. Therefore, the contribution of this work is two-fold. First, we employ an explicit and fully unsupervised disentanglement approach, which has previously only been used for representation learning, and show that it allows to obtain both superior voice conversion and content reconstruction. Second, we investigate simple and generic approaches to linearly adapt the length of a speech signal, and hence the speaking rate, to a target speaker and show that the proposed adaptation allows to increase the speaking rate similarity with respect to the target speaker

Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics

Rautenberg F, Kuhlmann M, Ebbers J, et al. Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics. In: Deutsche Gesellschaft für Akustik e.V. (DEGA), ed. Fortschritte der Akustik - DAGA 2023. Tagungsband. Berlin; 2023: 1409-1412.Popular speech disentanglement systems decompose a speech signal into a content and a speaker embedding, where a decoder reconstructs the input signal from these embeddings. Often, it is unknown, which information is encoded in the speaker embeddings. In this work, such a system is investigated on German speech data. We show that directions in the speaker embeddings space correlate with different acoustic signal properties that are known to be characteristics of a speaker, and manipulating these embeddings in that direction, the decoder synthesises a speech signal with modified acoustic properties