Renditen im Umlageverfahren: Anmerkungen zu einem 'einfachen Zusammenhang'

In der August-Ausgabe 1998 des WIRTSCHAFTSDIENST veröffentlichten wir einen Aufsatz von Dr. Hans H. Glismann und Ernst-Jürgen Hornf mit dem Titel „Renditen in der deutschen gesetzlichen Alterssicherung". Hierzu eine Replik von Stefan Eitenmüller und Winfried Hain sowie anschließend eine Erwiderung von Dr. Hans H. Glismann. --

MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data

Training of speech enhancement systems often does not incorporate knowledge of human perception and thus can lead to unnatural sounding results. Incorporating psychoacoustically motivated speech perception metrics as part of model training via a predictor network has recently gained interest. However, the performance of such predictors is limited by the distribution of metric scores that appear in the training data. In this work, we propose MetricGAN+/- (an extension of MetricGAN+, one such metric-motivated system) which introduces an additional network - a "de-generator" which attempts to improve the robustness of the prediction network (and by extension of the generator) by ensuring observation of a wider range of metric scores in training. Experimental results on the VoiceBank-DEMAND dataset show relative improvement in PESQ score of 3.8% (3.05 vs 3.22 PESQ score), as well as better generalisation to unseen noise and speech.Comment: 5 pages, 4 figures, Submitted to EUSIPCO 202

Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation

Speech dereverberation is an important stage in many speech technology applications. Recent work in this area has been dominated by deep neural network models. Temporal convolutional networks (TCNs) are deep learning models that have been proposed for sequence modelling in the task of dereverberating speech. In this work a weighted multi-dilation depthwise-separable convolution is proposed to replace standard depthwise-separable convolutions in TCN models. This proposed convolution enables the TCN to dynamically focus on more or less local information in its receptive field at each convolutional block in the network. It is shown that this weighted multi-dilation temporal convolutional network (WD-TCN) consistently outperforms the TCN across various model configurations and using the WD-TCN model is a more parameter efficient method to improve the performance of the model than increasing the number of convolutional blocks. The best performance improvement over the baseline TCN is 0.55 dB scale-invariant signal-to-distortion ratio (SISDR) and the best performing WD-TCN model attains 12.26 dB SISDR on the WHAMR dataset.Comment: Accepted at IWAENC 202

On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments

Speech separation remains an important topic for multi-speaker technology researchers. Convolution augmented transformers (conformers) have performed well for many speech processing tasks but have been under-researched for speech separation. Most recent state-of-the-art (SOTA) separation models have been time-domain audio separation networks (TasNets). A number of successful models have made use of dual-path (DP) networks which sequentially process local and global information. Time domain conformers (TD-Conformers) are an analogue of the DP approach in that they also process local and global context sequentially but have a different time complexity function. It is shown that for realistic shorter signal lengths, conformers are more efficient when controlling for feature dimension. Subsampling layers are proposed to further improve computational efficiency. The best TD-Conformer achieves 14.6 dB and 21.2 dB SISDR improvement on the WHAMR and WSJ0-2Mix benchmarks, respectively.Comment: Accepted at ASRU Workshop 202

Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation

Speech separation models are used for isolating individual speakers in many speech processing applications. Deep learning models have been shown to lead to state-of-the-art (SOTA) results on a number of speech separation benchmarks. One such class of models known as temporal convolutional networks (TCNs) has shown promising results for speech separation tasks. A limitation of these models is that they have a fixed receptive field (RF). Recent research in speech dereverberation has shown that the optimal RF of a TCN varies with the reverberation characteristics of the speech signal. In this work deformable convolution is proposed as a solution to allow TCN models to have dynamic RFs that can adapt to various reverberation times for reverberant speech separation. The proposed models are capable of achieving an 11.1 dB average scale-invariant signalto-distortion ratio (SISDR) improvement over the input signal on the WHAMR benchmark. A relatively small deformable TCN model of 1.3M parameters is proposed which gives comparable separation performance to larger and more computationally complex models.Comment: Accepted for ICASSP 202

Icefish spawning aggregation in the southern Weddell Sea

During the Continental Shelf Multidisciplinary Flux Study (COSMUS) expedition from February to March 2021 aboard RV Polarstern (expedition ID: PS124) (Hellmer & Holtappels, 2021), a large spawning aggregation of notothenioid icefish (Neopagetopsis ionah, Nybelin 1947) was discovered in the southern Weddell Sea. The CCAMLR community was informed of this discovery by Germany in COMM CIRC 22/10 and SC CIRC 22/08 earlier this year (17 January 2022). Purser et al. (2022) report on the spawning aggregation in Current Biology. Here we provide detailed information on the active fish nest aggregation and on additional icefish nesting sites observed in the Filchner Trough area (Knust & Schröder, 2014; Schröder, 2016; Riginella et al., 2021; Purser et al., 2022; Purser et al., in review)

Evaluation of the achievement of WSMPA conservation features and their target values for WSMPA Phase 1

At CCAMLR-38, the Delegation of the European Union and its Member States and Norway proposed that the Weddell Sea Marine Protected Area (WSMPA) should be adopted by CCAMLR in two phases: WSMPA Phase 1 and WSMPA Phase 2. A proposal to establish WSMPA Phase 1 has been submitted to CCAMLR-38. At this point, we provide brief information on the achievement of the WSMPA conservation features and their conservation targets for WSMPA Phase 1