Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection

Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often suffer from context bias and fuzzy localization, which hinder their ability to precisely and efficiently identify anomalies. To address these challenges, we propose a graph-centric framework, TempoLog, which leverages multi-scale temporal graph networks for discrete log anomaly detection. Unlike conventional methods, TempoLog constructs continuous-time dynamic graphs directly from event logs, eliminating the need for fixed-size window grouping. By representing log templates as nodes and their temporal relationships as edges, the framework dynamically captures both local and global dependencies across multiple temporal scales. Additionally, a semantic-aware model enhances detection by incorporating rich contextual information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art performance in event-level anomaly detection, significantly outperforming existing approaches in both accuracy and efficiency.

Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities

Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly. Existing methods commonly extract log sequence features using classical machine learning techniques to identify whether a new sequence is an anomaly or not. However, these classical approaches often require trade-offs between efficiency and accuracy. The advent of quantum machine learning (QML) offers a promising alternative. By transforming parts of classical machine learning computations into parameterized quantum circuits (PQCs), QML can significantly reduce the number of trainable parameters while maintaining accuracy comparable to classical counterparts. In this work, we introduce a unified framework, \ourframework{}, for evaluating QML models in the context of LogAD. This framework incorporates diverse log data, integrated QML models, and comprehensive evaluation metrics. State-of-the-art methods such as DeepLog, LogAnomaly, and LogRobust, along with their quantum-transformed counterparts, are included in our framework.Beyond standard metrics like F1 score, precision, and recall, our evaluation extends to factors critical to QML performance, such as specificity, the number of circuits, circuit design, and quantum state encoding. Using \ourframework{}, we conduct extensive experiments to assess the performance of these models and their quantum counterparts, uncovering valuable insights and paving the way for future research in QML model selection and design for LogAD.

SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios

The systematic evaluation of speech separation and enhancement models under moving sound source conditions typically requires extensive data comprising diverse scenarios. However, real-world datasets often contain insufficient data to meet the training and evaluation requirements of models. Although synthetic datasets offer a larger volume of data, their acoustic simulations lack realism. Consequently, neither real-world nor synthetic datasets effectively fulfill practical needs. To address these issues, we introduce SonicSim, a synthetic toolkit de-designed to generate highly customizable data for moving sound sources. SonicSim is developed based on the embodied AI simulation platform, Habitat-sim, supporting multi-level adjustments, including scene-level, microphone-level, and source-level, thereby generating more diverse synthetic data. Leveraging SonicSim, we constructed a moving sound source benchmark dataset, SonicSet, using the Librispeech, the Freesound Dataset 50k (FSD50K) and Free Music Archive (FMA), and 90 scenes from the Matterport3D to evaluate speech separation and enhancement models. Additionally, to validate the differences between synthetic data and real-world data, we randomly selected 5 hours of raw data without reverberation from the SonicSet validation set to record a real-world speech separation dataset, which was then compared with the corresponding synthetic datasets. Similarly, we utilized the real-world speech enhancement dataset RealMAN to validate the acoustic gap between other synthetic datasets and the SonicSet dataset for speech enhancement. The results indicate that the synthetic data generated by SonicSim can effectively generalize to real-world scenarios. Demo and code are publicly available at https://cslikai.cn/SonicSim/.

Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches

In real-world applications, it is challenging to build a speaker verification system that is simultaneously robust against common threats, including spoofing attacks, channel mismatch, and domain mismatch. Traditional automatic speaker verification (ASV) systems often tackle these issues separately, leading to suboptimal performance when faced with simultaneous challenges. In this paper, we propose an integrated framework that incorporates pair-wise learning and spoofing attack simulation into the meta-learning paradigm to enhance robustness against these multifaceted threats. This novel approach employs an asymmetric dual-path model and a multi-task learning strategy to handle ASV, anti-spoofing, and spoofing-aware ASV tasks concurrently. A new testing dataset, CNComplex, is introduced to evaluate system performance under these combined threats. Experimental results demonstrate that our integrated model significantly improves performance over traditional ASV systems across various scenarios, showcasing its potential for real-world deployment. Additionally, the proposed framework's ability to generalize across different conditions highlights its robustness and reliability, making it a promising solution for practical ASV applications.

InstructSing: High-Fidelity Singing Voice Generation via Instructing Yourself

It is challenging to accelerate the training process while ensuring both high-quality generated voices and acceptable inference speed. In this paper, we propose a novel neural vocoder called InstructSing, which can converge much faster compared with other neural vocoders while maintaining good performance by integrating differentiable digital signal processing and adversarial training. It includes one generator and two discriminators. Specifically, the generator incorporates a harmonic-plus-noise (HN) module to produce 8kHz audio as an instructive signal. Subsequently, the HN module is connected with an extended WaveNet by an UNet-based module, which transforms the output of the HN module to a latent variable sequence containing essential periodic and aperiodic information. In addition to the latent sequence, the extended WaveNet also takes the mel-spectrogram as input to generate 48kHz high-fidelity singing voices. In terms of discriminators, we combine a multi-period discriminator, as originally proposed in HiFiGAN, with a multi-resolution multi-band STFT discriminator. Notably, InstructSing achieves comparable voice quality to other neural vocoders but with only one-tenth of the training steps on a 4 NVIDIA V100 GPU machine\footnote{{Demo page: \href{https://wavelandspeech.github.io/instructsing/}{\texttt{https://wavelandspeech.github.io/inst\\ructsing/}}}}. We plan to open-source our code and pretrained model once the paper get accepted.

A Benchmark for Multi-speaker Anonymization

Privacy-preserving voice protection approaches primarily suppress privacy-related information derived from paralinguistic attributes while preserving the linguistic content. Existing solutions focus on single-speaker scenarios. However, they lack practicality for real-world applications, i.e., multi-speaker scenarios. In this paper, we present an initial attempt to provide a multi-speaker anonymization benchmark by defining the task and evaluation protocol, proposing benchmarking solutions, and discussing the privacy leakage of overlapping conversations. Specifically, ideal multi-speaker anonymization should preserve the number of speakers and the turn-taking structure of the conversation, ensuring accurate context conveyance while maintaining privacy. To achieve that, a cascaded system uses speaker diarization to aggregate the speech of each speaker and speaker anonymization to conceal speaker privacy and preserve speech content. Additionally, we propose two conversation-level speaker vector anonymization methods to improve the utility further. Both methods aim to make the original and corresponding pseudo-speaker identities of each speaker unlinkable while preserving or even improving the distinguishability among pseudo-speakers in a conversation. The first method minimizes the differential similarity across speaker pairs in the original and anonymized conversations to maintain original speaker relationships in the anonymized version. The other method minimizes the aggregated similarity across anonymized speakers to achieve better differentiation between speakers. Experiments conducted on both non-overlap simulated and real-world datasets demonstrate the effectiveness of the multi-speaker anonymization system with the proposed speaker anonymizers. Additionally, we analyzed overlapping speech regarding privacy leakage and provide potential solutions.

HAM-TTS: Hierarchical Acoustic Modeling for Token-Based Zero-Shot Text-to-Speech with Model and Data Scaling

Token-based text-to-speech (TTS) models have emerged as a promising avenue for generating natural and realistic speech, yet they grapple with low pronunciation accuracy, speaking style and timbre inconsistency, and a substantial need for diverse training data. In response, we introduce a novel hierarchical acoustic modeling approach complemented by a tailored data augmentation strategy and train it on the combination of real and synthetic data, scaling the data size up to 650k hours, leading to the zero-shot TTS model with 0.8B parameters. Specifically, our method incorporates a latent variable sequence containing supplementary acoustic information based on refined self-supervised learning (SSL) discrete units into the TTS model by a predictor. This significantly mitigates pronunciation errors and style mutations in synthesized speech. During training, we strategically replace and duplicate segments of the data to enhance timbre uniformity. Moreover, a pretrained few-shot voice conversion model is utilized to generate a plethora of voices with identical content yet varied timbres. This facilitates the explicit learning of utterance-level one-to-many mappings, enriching speech diversity and also ensuring consistency in timbre. Comparative experiments (Demo page: https://anonymous.4open.science/w/ham-tts/)demonstrate our model's superiority over VALL-E in pronunciation precision and maintaining speaking style, as well as timbre continuity.

CrossSinger: A Cross-Lingual Multi-Singer High-Fidelity Singing Voice Synthesizer Trained on Monolingual Singers

It is challenging to build a multi-singer high-fidelity singing voice synthesis system with cross-lingual ability by only using monolingual singers in the training stage. In this paper, we propose CrossSinger, which is a cross-lingual singing voice synthesizer based on Xiaoicesing2. Specifically, we utilize International Phonetic Alphabet to unify the representation for all languages of the training data. Moreover, we leverage conditional layer normalization to incorporate the language information into the model for better pronunciation when singers meet unseen languages. Additionally, gradient reversal layer (GRL) is utilized to remove singer biases included in lyrics since all singers are monolingual, which indicates singer's identity is implicitly associated with the text. The experiment is conducted on a combination of three singing voice datasets containing Japanese Kiritan dataset, English NUS-48E dataset, and one internal Chinese dataset. The result shows CrossSinger can synthesize high-fidelity songs for various singers with cross-lingual ability, including code-switch cases.

Improving Generalization Ability of Countermeasures for New Mismatch Scenario by Combining Multiple Advanced Regularization Terms

The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch scenario in which fake audio may be generated from real audio with unseen genres has not been studied thoroughly. To this end, we first use five different vocoders to create a new dataset called CN-Spoof based on the CN-Celeb1\&2 datasets. Then, we design two auxiliary objectives for regularization via meta-optimization and a genre alignment module, respectively, and combine them with the main anti-spoofing objective using learnable weights for multiple loss terms. The results on our cross-genre evaluation dataset for anti-spoofing show that the proposed method significantly improved the generalization ability of the countermeasures compared with the baseline system in the genre mismatch scenario.

Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognit

Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR system on smart devices with the help of Transformer-based models. While these models still have the disadvantage of requiring a large number of model parameters. To overcome the drawback of universal Transformer models for the application of ASR on edge devices, we propose a solution that can reuse the block in Transformer models for the occasion of the small footprint ASR system, which meets the objective of accommodating resource limitations without compromising recognition accuracy. Specifically, we design a novel block-reusing strategy for speech Transformer (BRST) to enhance the effectiveness of parameters and propose an adapter module (ADM) that can produce a compact and adaptable model with only a few additional trainable parameters accompanying each reusing block. We conducted an experiment with the proposed method on the public AISHELL-1 corpus, and the results show that the proposed approach achieves the character error rate (CER) of 9.3%/6.63% with only 7.6M/8.3M parameters without and with the ADM, respectively. In addition, we also make a deeper analysis to show the effect of ADM in the general block-reusing method.