Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation

Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music

Shift-Invariant Kernel Additive Modelling for Audio Source Separation

A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of-the-art approaches are supervised methods trained on large datasets, interest in non-data-driven approaches such as Kernel Additive Modelling (KAM) remains high due to their interpretability and adaptability. KAM performs the separation of a given source applying robust statistics on the time-frequency bins selected by a source-specific kernel function, commonly the K-NN function. This choice assumes that the source of interest repeats in both time and frequency. In practice, this assumption does not always hold. Therefore, we introduce a shift-invariant kernel function capable of identifying similar spectral content even under frequency shifts. This way, we can considerably increase the amount of suitable sound material available to the robust statistics. While this leads to an increase in separation performance, a basic formulation, however, is computationally expensive. Therefore, we additionally present acceleration techniques that lower the overall computational complexity.Comment: Feedback is welcom

Towards a Framework for the Discovery of Collections of Live Music Recordings and Artefacts on the Semantic Web

This paper introduces a platform for the representation and discovery of live music recordings and associated artefacts based on a dedicated data model. We demonstrate our technology by implementing a Web-based discovery tool for the Grateful Dead collection of the Internet Archive, a large collection of concert recordings annotated with editorial metadata. We represent this information using a Linked Data model complemented with data aggregated from several additional Web resources discussing and describing these events. These data include descriptions and images of physical artefacts such as tickets, posters and fan photos, as well as other information, e.g. about location and weather. The system uses signal processing techniques for the analysis and alignment of the digital recordings. During the discovery, users can juxtapose and compare different recordings of a given concert, or different performances of a given song by interactively blending between them

Robustness of Adversarial Attacks in Sound Event Classification

An adversarial attack is a method to generate perturbations to the input of a machine learning model in order to make the output of the model incorrect. The perturbed inputs are known as adversarial examples. In this paper, we investigate the robustness of adversarial examples to simple input transformations such as mp3 compression, resampling, white noise and reverb in the task of sound event classification. By performing this analysis, we aim to provide insight on strengths and weaknesses in current adversarial attack algorithms as well as provide a baseline for defenses against adversarial attacks. Our work shows that adversarial attacks are not robust to simple input transformations. White noise is the most consistent method to defend against adversarial attacks with a success rate of $73.72\%$ averaged across all models and attack algorithms.23924

Systemic Therapy of Bronchioloalveolar Carcinoma: Results of the First IASLC/ASCO Consensus Conference on Bronchioloalveolar Carcinoma

Introduction:Bronchioloalveolar carcinoma (BAC) is a subtype of adenocarcinoma of the lung with unique pathological, clinical, and molecular characteristics.Methods:This consensus conference group reviewed studies performed specifically in BAC and data from patients with BAC who were included in clinical trials of all non–small-cell lung cancer (NSCLC) subtypes.Results:Although BAC as defined by the World Health Organization represents less than 5% of adenocarcinomas, as many as 20% of adenocarcinomas have BAC features. These latter tumors are more likely to have mutations in the epidermal growth factor receptor (EGFR) gene and to be sensitive to the EGFR tyrosine kinase inhibitors gefitinib and erlotinib. Although most patients are men and have a history of smoking cigarettes, proportionally more are women and never smokers. Patients with BAC are routinely treated with drugs and regimens appropriate for patients with all subtypes of adenocarcinoma of the lung; four studies have been performed specifically in this disease.Conclusions:There is insufficient evidence to confirm or refute the assertion that the sensitivity of BAC to chemotherapy is different from that of other lung cancer histologic types. The unique clinical and molecular characteristics associated with BAC led this panel to conclude that future clinical trials should be designed specifically for persons with BAC. Recommendations for trial design and research questions are proposed

Syncopation and the Score

The first and second authors were supported by an EPSRC DTA (www.epsrc.ac.uk) studentship

Randomized Phase III Trial Comparing Single-Agent Paclitaxel Poliglumex (CT-2103, PPX) with Single-Agent Gemcitabine or Vinorelbine for the Treatment of PS 2 Patients with Chemotherapy-Naïve Advanced Non-small Cell Lung Cancer

BACKGROUND: Patients with advanced non-small cell lung cancer (NSCLC) and impaired performance status (PS >or= 2) have limited life expectancies and decreased tolerance for drug-induced toxicities. Current treatment guidelines indicate that PS 2 patients benefit from systemic therapy. Further refinement of treatment in these patients requires reduction of treatment-associated toxicities while maintaining or improving efficacy. Paclitaxel poliglumex (PPX), a macromolecular polymer-drug conjugate of paclitaxel and poly-l-glutamic acid, may enhance the therapeutic index of paclitaxel. METHODS: Chemotherapy-naive PS 2 patients with advanced NSCLC randomly received single-agent PPX (175 mg/m) or a comparator (single-agent vinorelbine or gemcitabine). The primary end point of this study was overall survival. RESULTS: Overall survival was similar between treatment arms (hazard ratio [HR] = 0.95; log-rank p = 0.686). Median and 1-year survival were 7.3 months and 26%, respectively, for PPX versus 6.6 months and 26% for the control arm. There was a nonsignificant trend toward improved survival in women in the PPX arm compared with standard single agents (HR = 0.65; p = 0.069). The most frequent grade 3/4 adverse events in the treatment versus control arm were dyspnea (13% versus 17%, respectively) and fatigue (10% versus 9%). Grade 3/4 neutropenia and anemia were reduced in the PPX arm (2% versus 8% and 3% versus 9%, respectively). Neuropathy, a taxane-specific toxicity, was more common in the PPX arm; grade 3 neuropathy was limited to 3%. CONCLUSIONS: Single-agent PPX, dosed at 175 mg/m, is active and well tolerated in PS 2 patients with advanced NSCLC. Patients on PPX required fewer red blood cell transfusions, hematopoietic growth factors, opioid analgesics, and clinic visits than patients receiving gemcitabine or vinorelbine