This work presents advancements in audio pretraining objectives designed to generate semantically rich embeddings, capable of addressing a wide range of audio-related tasks. Despite significant progress in the field, current methods often emphasize full fine-tuning in downstream applications, which can obscure the true potential of pretrained audio encoders. In this study, we present an audio encoder that achieves stateof-the-art (SOTA) performances in both fine-tuning and linear probing, utilizing a carefully curated set of pragmatic techniques. Building on previous research, we incorporate masked prediction and introduce SpecAug within a curriculum masking strategy at the patch level, which progressively increases training difficulty, along with a mask-aware position bias. To comprehensively assess the encoder’s capabilities, we examine the impact of scaling both the dataset size and model capacity, conducting linear probing evaluations while keeping the encoder frozen as well as full fine-tuning. Our model demonstrates superior performance compared to recent SOTA methods across various downstream tasks. Additionally, we explore the potential of tokenizing the resulting audio embeddings for use as discrete inputs, enhancing our understanding of the model’s capabilities.

Text-to-audio generation synthesizes realistic sounds or music given a natural language prompt. Diffusion-based frameworks, including the Tango and the AudioLDM series, represent the state-of-the-art in text-to-audio generation. Despite achieving high audio fidelity, they incur significant inference latency due to the slow diffusion sampling process. MAGNET, a mask-based model operating on discrete tokens, addresses slow inference through iterative mask-based parallel decoding. However, its audio quality still lags behind that of diffusion-based models. In this work, we introduce IMPACT, a text-to-audio generation framework that achieves high performance in audio quality and fidelity while ensuring fast inference. IMPACT utilizes iterative mask-based parallel decoding in a continuous latent space powered by diffusion modeling. This approach eliminates the fidelity constraints of discrete tokens while maintaining competitive inference speed. Results on AudioCaps demonstrate that IMPACT achieves state-of-the-art performance on key metrics including Fréchet Distance (FD) and Fréchet Audio Distance (FAD) while significantly reducing latency compared to prior models. The project website is available at https://audio-impact.github.io/.

Autoregressive next-token prediction with the Transformer decoder has become a de facto standard in large language models (LLMs), achieving remarkable success in Natural Language Processing (NLP) at scale. Extending this paradigm to audio poses unique challenges due to its inherently continuous nature. We research audio generation with a causal language model (LM) without discrete tokens. We leverage token-wise diffusion to model the continuous distribution of the next continuous-valued token. Our approach delivers significant improvements over previous discrete solution, AudioGen, achieving 20% and 40% relative gains on AudioCaps in Frechet Audio Distance (FAD) and Kullback-Leibler (KL) divergence, respectively. Additionally, we propose a novel masked next-token prediction task that incorporates masked prediction into the causal LM framework. On AudioCaps, the innovation yields 41% and 33% relative FAD improvements over AudioGen Base (285M) and AudioGen Large (1B) models, respectively, and is on par with the state-of-the-art (SOTA) diffusion models. Furthermore, we achieve these results with significantly fewer parameters—193M for our Base and 462M for our Large models.

Cross-triggering is a critical problem for applications of audio event detection (AED), particularly in low-resource settings. However, not much attention (if not none) has been paid to this problem in the AED research community. In this work, we tackle this problem via a regularization approach. We propose a regularizer, namely mutual exclusivity regularizer, that is able to enforce pairwise exclusivity between two event classes when they do not co-occur. When the regularizer is added to the loss function for network training, in effect, the increase in the score of one event class will result in the decrease of the other and vice versa. To quantify the effectiveness of the proposed regularizer, we developed an AED system based on convolutional neural network (CNN) for the detection of hand clap and door knock, two transient audio events that share similar spectro-temporal profiles, and conducted experiments on a large-scale real-world dataset (around 274.2 hours). The experimental results show that the proposed approach is able to largely mitigate the cross-triggering issue in various experimental settings. Furthermore, the reduction in cross-triggering, as a result, leads to improvement in the detection performance.

This paper proposes a novel, efficient transfer learning method, called Scalable Weight Reparametrization (SWR) that is efficient and effective for multiple downstream tasks. Efficient transfer learning involves utilizing a pre-trained model trained on a larger dataset and repurposing it for downstream tasks with the aim of maximizing the reuse of the pre-trained model. However, previous works have led to an increase in updated parameters and task-specific modules, resulting in more computations, especially for tiny models. Additionally, there has been no practical consideration for controlling the number of updated parameters. To address these issues, we suggest learning a policy network that can decide where to reparametrize the pre-trained model, while adhering to a given constraint for the number of updated parameters. The policy network is only used during the transfer learning process and not afterward. As a result, our approach attains state-of-the-art performance in a proposed multi-lingual keyword spotting and a standard benchmark, ImageNet-to-Sketch, while requiring zero additional computations and significantly fewer additional parameters.

In few-shot open-set recognition (FSOSR), a network learns to recognize closed-set samples with a few support samples while rejecting open-set samples with no class cue. Unlike conventional OSR, the FSOSR considers more practical open worlds where a closed-set class can be selected as an open-set class in another testing (task) and vice versa. Existing FSOSR methods have commonly represented the open set with task-dependent extra modules. These modules decently handle the varied closed and open classes but accompany inevitable complexity increase. This paper shows that a single open-set prototype can represent open-set samples when it satisfies a specific relation in metric space: closest to open-set, and simultaneously second nearest to close-set. We propose a task-agnostic open-set prototype with distance scaling factors and design loss terms. We extensively analyze the proposed components to demonstrate their importance. Our method achieves state-of-the-art results on miniImageNet and tieredImageNet, respectively, without task-dependent extra modules.

Keyword spotting is the task of detecting a keyword in streaming audio. Conventional keyword spotting targets predefined keywords classification, but there is growing attention in few-shot (query-by-example) keyword spotting, e.g., N-way classification given M-shot support samples. Moreover, in real-world scenarios, there can be utterances from unexpected categories (open-set) which need to be rejected rather than classified as one of the N classes. Combining the two needs, we tackle few-shot open-set keyword spotting with a new benchmark setting, named splitGSC. We propose episode-known dummy prototypes based on metric learning to detect an open-set better and introduce a simple and powerful approach, Dummy Prototypical Networks (D-ProtoNets). Our D-ProtoNets shows clear margins compared to recent few-shot open-set recognition (FSOSR) approaches in the suggested splitGSC. We also verify our method on a standard benchmark, miniImageNet, and D-ProtoNets shows the state-of-the-art open-set detection rate in FSOSR.

While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features. However, unlike image processing, we analyze that domain-relevant information in an audio feature is dominant in frequency statistics rather than channel statistics. Motivated by our analysis, we introduce Relaxed Instance Frequency-wise Normalization (RFN): a plug-and-play, explicit normalization module along the frequency axis which can eliminate instance-specific domain discrepancy in an audio feature while relaxing undesirable loss of useful discriminative information. Empirically, simply adding RFN to networks shows clear margins compared to previous domain generalization approaches on acoustic scene classification and yields improved robustness for multiple audio devices. Especially, the proposed RFN won the DCASE2021 challenge TASK1A, low-complexity acoustic scene classification with multiple devices, with a clear margin, and RFN is an extended work of our technical report.

Keyword spotting (KWS) plays an essential role in enabling speech-based user interaction on smart devices, and conventional KWS (C-KWS) approaches have concentrated on detecting user-agnostic pre-defined keywords.However, in practice, most user interactions come from target users enrolled in the device which motivates to construct personalized keyword spotting.We design two personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and ( 2) Target user Only KWS (TO-KWS).To solve the tasks, we propose personalized keyword spotting through multi-task learning (PK-MTL) that consists of multi-task learning and task-adaptation.First, we introduce applying multi-task learning on keyword spotting and speaker verification to leverage user information to the keyword spotting system.Next, we design task-specific scoring functions to adapt to the personalized KWS tasks thoroughly.We evaluate our framework on conventional and personalized scenarios, and the results show that PK-MTL can dramatically reduce the false alarm rate, especially in various practical scenarios.

Keyword spotting is an important research field because it plays a key role in device wake-up and user interaction on smart devices.However, it is challenging to minimize errors while operating efficiently in devices with limited resources such as mobile phones.We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load.Our method configures most of the residual functions as 1D temporal convolution while still allows 2D convolution together using a broadcasted-residual connection that expands temporal output to frequency-temporal dimension.This residual mapping enables the network to effectively represent useful audio features with much less computation than conventional convolutional neural networks.We also propose a novel network architecture, Broadcasting-residual network (BC-ResNet), based on broadcasted residual learning and describe how to scale up the model according to the target device's resources.BC-ResNets achieve state-of-the-art 98.0% and 98.7% top-1 accuracy on Google speech command datasets v1 and v2, respectively, and consistently outperform previous approaches, using fewer computations and parameters.Code is available at https://github.com/Qualcomm-

A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system which is user-specific. The proposed system consists of two main steps: query enrollment and testing. In query enrollment step, phonetic posteriors are output by a small-footprint automatic speech recognition model based on connectionist temporal classification. Using the phonetic-level posteriorgram, hypothesis graph of finite-state transducer (FST) is built, thus can enroll any keywords thus avoiding an out-of-vocabulary problem. In testing, a log-likelihood is scored for input audio using the FST. We propose a threshold prediction method while using the user-specific keyword hypothesis only. The system generates query-specific negatives by rearranging each query utterance in waveform. The threshold is decided based on the enrollment queries and generated negatives. We tested two keywords in English, and the proposed work shows promising performance while preserving simplicity.

Multi-head attention mechanism is capable of learning various representations from sequential data while paying attention to different subsequences, e.g., word-pieces or syllables in a spoken word. From the subsequences, it retrieves richer information than a single-head attention which only summarizes the whole sequence into one context vector. However, a naive use of the multi-head attention does not guarantee such richness as the attention heads may have positional and representational redundancy. In this paper, we propose a regularization technique for multi-head attention mechanism in an end-to-end neural keyword spotting system. Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models. In addition, intra-head contextual non-orthogonality regularization encourages each attention head to have similar representations across keyword examples, which helps classification by reducing feature variability. The experimental results demonstrate that the proposed regularization technique significantly improves the keyword spotting performance for the keyword “Hey Snapdragon”.