@article{35602,
  abstract     = {{Continuous Speech Separation (CSS) has been proposed to address speech overlaps during the analysis of realistic meeting-like conversations by eliminating any overlaps before further processing.
CSS separates a recording of arbitrarily many speakers into a small number of overlap-free output channels, where each output channel may contain speech of multiple speakers.
This is often done by applying a conventional separation model trained with Utterance-level Permutation Invariant Training (uPIT), which exclusively maps a speaker to an output channel, in sliding window approach called stitching.
Recently, we introduced an alternative training scheme called Graph-PIT that teaches the separation network to directly produce output streams in the required format without stitching.
It can handle an arbitrary number of speakers as long as never more of them overlap at the same time than the separator has output channels.
In this contribution, we further investigate the Graph-PIT training scheme.
We show in extended experiments that models trained with Graph-PIT also work in challenging reverberant conditions.
Models trained in this way are able to perform segment-less CSS, i.e., without stitching, and achieve comparable and often better separation quality than the conventional CSS with uPIT and stitching.
We simplify the training schedule for Graph-PIT with the recently proposed Source Aggregated Signal-to-Distortion Ratio (SA-SDR) loss.
It eliminates unfavorable properties of the previously used A-SDR loss and thus enables training with Graph-PIT from scratch.
Graph-PIT training relaxes the constraints w.r.t. the allowed numbers of speakers and speaking patterns which allows using a larger variety of training data.
Furthermore, we introduce novel signal-level evaluation metrics for meeting scenarios, namely the source-aggregated scale- and convolution-invariant Signal-to-Distortion Ratio (SA-SI-SDR and SA-CI-SDR), which are generalizations of the commonly used SDR-based metrics for the CSS case.}},
  author       = {{von Neumann, Thilo and Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}},
  issn         = {{2329-9290}},
  journal      = {{IEEE/ACM Transactions on Audio, Speech, and Language Processing}},
  keywords     = {{Continuous Speech Separation, Source Separation, Graph-PIT, Dynamic Programming, Permutation Invariant Training}},
  pages        = {{576--589}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria}}},
  doi          = {{10.1109/taslp.2022.3228629}},
  volume       = {{31}},
  year         = {{2023}},
}

@inproceedings{48855,
  abstract     = {{Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.}},
  author       = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
  booktitle    = {{Learning and Intelligent Optimization}},
  isbn         = {{978-3-030-92120-0}},
  keywords     = {{Dynamic programming, Exact counting, Sampling, Zero-one knapsack problem}},
  pages        = {{40–54}},
  publisher    = {{Springer-Verlag}},
  title        = {{{Exact Counting and~Sampling of Optima for the Knapsack Problem}}},
  doi          = {{10.1007/978-3-030-92121-7_4}},
  year         = {{2021}},
}

@inproceedings{17651,
  abstract     = {{Consider mitigating the effects of denial of service or of malicious traffic in networks by deleting edges. Edge deletion reduces the DoS or the number of the malicious flows, but it also inadvertently removes some of the desired flows. To model this important problem, we formulate two problems: (1) remove all the undesirable flows while minimizing the damage to the desirable ones and (2) balance removing the undesirable flows and not removing too many of the desirable flows. We prove these problems are equivalent to important theoretical problems, thereby being important not only practically but also theoretically, and very hard to approximate in a general network. We employ reductions to nonetheless approximate the problem and also provide a greedy approximation. When the network is a tree, the problems are still MAX SNP-hard, but we provide a greedy-based 2l-approximation algorithm, where l is the longest desirable flow. We also provide an algorithm, approximating the first and the second problem within {\$}{\$}2 {\backslash}sqrt{\{} 2{\backslash}left| E {\backslash}right| {\}}{\$}{\$}and {\$}{\$}2 {\backslash}sqrt{\{}2 ({\backslash}left| E {\backslash}right| + {\backslash}left| {\backslash}text {\{}undesirable flows{\}} {\backslash}right| ){\}}{\$}{\$}, respectively, where E is the set of the edges of the network. We also provide a fixed-parameter tractable (FPT) algorithm. Finally, if the tree has a root such that every flow in the tree flows on the path from the root to a leaf, we solve the problem exactly using dynamic programming.}},
  author       = {{Polevoy, Gleb and Trajanovski, Stojan and Grosso, Paola and de Laat, Cees}},
  booktitle    = {{Combinatorial Optimization and Applications}},
  editor       = {{Kim, Donghyun and Uma, R. N. and Zelikovsky, Alexander}},
  isbn         = {{978-3-030-04651-4}},
  keywords     = {{flow, Red-Blue Set Cover, Positive-Negative Partial Set Cover, approximation, tree, MAX SNP-hard, root, leaf, dynamic programming, FPT}},
  pages        = {{217--232}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Removing Undesirable Flows by Edge Deletion}}},
  year         = {{2018}},
}