arXiv subjects

Yuki Okamoto

Publications and source records attributed to Yuki Okamoto.

At least 19 recordsLinked to original sources

arXiv2026

Human-CLAP: Human-perception-based contrastive language-audio pretraining

Contrastive language-audio pretraining (CLAP) is widely used for audio generation and recognition tasks. For example, CLAPScore, which utilizes the similarity of CLAP embeddings, has been a major metric for the evaluation of the relevance between audio and text in text-to-audio. However, the relationship between CLAPScore and human subjective evaluation scores is still unclarified. We show that CLAPScore has a low correlation with human subjective evaluation scores. Additionally, we propose a human-perception-based CLAP called Human-CLAP by training a contrastive language-audio model using the subjective evaluation score. In our experiments, the results indicate that our Human-CLAP improved the Spearman's rank correlation coefficient (SRCC) between the CLAPScore and the subjective evaluation scores by more than 0.25 compared with the conventional CLAP.

eess.AS↗

arXiv2025

Spatial-CLAP: Learning Spatially-Aware audio--text Embeddings for Multi-Source Conditions

Contrastive language--audio pretraining (CLAP) has achieved remarkable success as an audio--text embedding framework, but existing approaches are limited to monaural or single-source conditions and cannot fully capture spatial information. The central challenge in modeling spatial information lies in multi-source conditions, where the correct correspondence between each sound source and its location is required. To tackle this problem, we propose Spatial-CLAP, which introduces a content-aware spatial encoder that enables spatial representations coupled with audio content. We further propose spatial contrastive learning (SCL), a training strategy that explicitly enforces the learning of the correct correspondence and promotes more reliable embeddings under multi-source conditions. Experimental evaluations, including downstream tasks, demonstrate that Spatial-CLAP learns effective embeddings even under multi-source conditions, and confirm the effectiveness of SCL. Moreover, evaluation on unseen three-source mixtures highlights the fundamental distinction between conventional single-source training and our proposed multi-source training paradigm. These findings establish a new paradigm for spatially-aware audio--text embeddings.

cs.SD↗

arXiv2025

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio embedding Sequences

We propose a novel objective evaluation metric for synthesized audio in text-to-audio (TTA), aiming to improve the performance of TTA models. In TTA, subjective evaluation of the synthesized sound is an important, but its implementation requires monetary costs. Therefore, objective evaluation such as mel-cepstral distortion are used, but the correlation between these objective metrics and subjective evaluation values is weak. Our proposed objective evaluation metric, AudioBERTScore, calculates the similarity between embedding of the synthesized and reference sounds. The method is based not only on the max-norm used in conventional BERTScore but also on the $p$-norm to reflect the non-local nature of environmental sounds. Experimental results show that scores obtained by the proposed method have a higher correlation with subjective evaluation values than conventional metrics.

cs.SD↗

arXiv2025

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio

In text-to-audio (TTA) research, the relevance between input text and output audio is an important evaluation aspect. Traditionally, it has been evaluated from both subjective and objective perspectives. However, subjective evaluation is costly in terms of money and time, and objective evaluation is unclear regarding the correlation to subjective evaluation scores. In this study, we construct RELATE, an open-sourced dataset that subjectively evaluates the relevance. Also, we benchmark a model for automatically predicting the subjective evaluation score from synthesized audio. Our model outperforms a conventional CLAPScore model, and that trend extends to many sound categories.

cs.SD↗

arXiv2025

Roles of Non-switchable Domains and Internal Bias in Electrocaloric and Pyroelectric effects

Solid-state cooling and energy harvesting via pyroelectric effect (PEE) and electrocaloric effect (ECE) in ferroelectric thin films could be enhanced beyond their intrinsic ferroelectric response by exploiting the recently observed direction-dependent enhancement of the PEE; however, its microscopic origin remains unknown. Herein, we report direct hysteresis measurements of pyrocurrent ($I_{\rm p}$) and ECE-induced temperature change versus bias voltage in 1-$μ$m-thick Pb(Zr$_{0.65}$Ti$_{0.35}$)O$_3$ capacitors. Both hysteresis loops exhibit pronounced asymmetries along the voltage and response axes. By superimposing direct current voltage offsets, we isolate a residual $I_{\rm p}$-axis shift, revealing a contribution of non-switchable ferroelectric polarization. This non-switchable polarization can be converted into switchable polarization via poling with bipolar triangular pulses, confirming the governing role of defect-induced domain pinning. After 100 pulses, time-dependent aging was observed for pyroelectric and electrocaloric responses, with the switchable contribution markedly decaying and the non-switchable component remaining nearly constant, indicating partial repinning. The change in voltage-axis shift agrees well with the ratio of non-switchable to switchable polarization, demonstrating that voltage shift also arises from pinned domains. These insights clarify the critical role of non-switchable polarization in the PEE and ECE performance, suggesting strategies to optimize the directional response in ferroelectric devices through controlled poling and defect engineering.

cond-mat.mtrl-sci↗

arXiv2025

Sound Scene Synthesis at the DCASE 2024 Challenge

This paper presents Task 7 at the DCASE 2024 Challenge: sound scene synthesis. Recent advances in sound synthesis and generative models have enabled the creation of realistic and diverse audio content. We introduce a standardized evaluation framework for comparing different sound scene synthesis systems, incorporating both objective and subjective metrics. The challenge attracted four submissions, which are evaluated using the Fréchet Audio Distance (FAD) and human perceptual ratings. Our analysis reveals significant insights into the current capabilities and limitations of sound scene synthesis systems, while also highlighting areas for future improvement in this rapidly evolving field.

cs.AI↗

arXiv2024

Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation

Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene Synthesis challenge held as part of the Detection and Classification of Acoustic Scenes and Events 2024. We present an evaluation protocol combining objective metric, namely Fréchet Audio Distance, with perceptual assessments, utilizing a structured prompt format to enable diverse captions and effective evaluation. Our analysis reveals varying performance across sound categories and model architectures, with larger models generally excelling but innovative lightweight approaches also showing promise. The strong correlation between objective metrics and human ratings validates our evaluation approach. We discuss outcomes in terms of audio quality, controllability, and architectural considerations for text-to-audio synthesizers, providing direction for future research.

cs.SD↗

arXiv2024

Construction and Analysis of Impression Caption Dataset for Environmental Sounds

Some datasets with the described content and order of occurrence of sounds have been released for conversion between environmental sound and text. However, there are very few texts that include information on the impressions humans feel, such as "sharp" and "gorgeous," when they hear environmental sounds. In this study, we constructed a dataset with impression captions for environmental sounds that describe the impressions humans have when hearing these sounds. We used ChatGPT to generate impression captions and selected the most appropriate captions for sound by humans. Our dataset consists of 3,600 impression captions for environmental sounds. To evaluate the appropriateness of impression captions for environmental sounds, we conducted subjective and objective evaluations. From our evaluation results, we indicate that appropriate impression captions for environmental sounds can be generated.

cs.SD↗

arXiv2024

Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant

This paper explores whether considering alternative domain-specific embeddings to calculate the Fréchet Audio Distance (FAD) metric can help the FAD to correlate better with perceptual ratings of environmental sounds. We used embeddings from VGGish, PANNs, MS-CLAP, L-CLAP, and MERT, which are tailored for either music or environmental sound evaluation. The FAD scores were calculated for sounds from the DCASE 2023 Task 7 dataset. Using perceptual data from the same task, we find that PANNs-WGM-LogMel produces the best correlation between FAD scores and perceptual ratings of both audio quality and perceived fit with a Spearman correlation higher than 0.5. We also find that music-specific embeddings resulted in significantly lower results. Interestingly, VGGish, the embedding used for the original Fréchet calculation, yielded a correlation below 0.1. These results underscore the critical importance of the choice of embedding for the FAD metric design.

cs.SD↗

arXiv2023

Foley Sound Synthesis at the DCASE 2023 Challenge

The addition of Foley sound effects during post-production is a common technique used to enhance the perceived acoustic properties of multimedia content. Traditionally, Foley sound has been produced by human Foley artists, which involves manual recording and mixing of sound. However, recent advances in sound synthesis and generative models have generated interest in machine-assisted or automatic Foley synthesis techniques. To promote further research in this area, we have organized a challenge in DCASE 2023: Task 7 - Foley Sound Synthesis. Our challenge aims to provide a standardized evaluation framework that is both rigorous and efficient, allowing for the evaluation of different Foley synthesis systems. We received 17 submissions, and performed both objective and subjective evaluation to rank them according to three criteria: audio quality, fit-to-category, and diversity. Through this challenge, we hope to encourage active participation from the research community and advance the state-of-the-art in automatic Foley synthesis. In this technical report, we provide a detailed overview of the Foley sound synthesis challenge, including task definition, dataset, baseline, evaluation scheme and criteria, challenge result, and discussion.

cs.SD↗

arXiv2023

Environmental sound synthesis from vocal imitations and sound event labels

One way of expressing an environmental sound is using vocal imitations, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice. We can effectively express the features of environmental sounds, such as rhythm and pitch, using vocal imitations, which cannot be expressed by conventional input information, such as sound event labels, images, or texts, in an environmental sound synthesis model. In this paper, we propose a framework for environmental sound synthesis from vocal imitations and sound event labels based on a framework of a vector quantized encoder and the Tacotron2 decoder. Using vocal imitations is expected to control the pitch and rhythm of the synthesized sound, which only sound event labels cannot control. Our objective and subjective experimental results show that vocal imitations effectively control the pitch and rhythm of synthesized sounds.

cs.SD↗

arXiv2023

CAPTDURE: Captioned Sound Dataset of Single Sources

In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model training. However, when we collect the captions for multiple-source sound, it is not easy to collect detailed captions for each sound source, such as the number of sound occurrences and timbre. Therefore, it is difficult to extract only the single-source target sound by the model-training method using a conventional captioned sound dataset. In this work, we constructed a dataset with captions for a single-source sound named CAPTDURE, which can be used in various tasks such as environmental sound separation and synthesis. Our dataset consists of 1,044 sounds and 4,902 captions. We evaluated the performance of environmental sound extraction using our dataset. The experimental results show that the captions for single-source sounds are effective in extracting only the single-source target sound from the mixture sound.

cs.SD↗

arXiv2022

Visual onoma-to-wave: environmental sound synthesis from visual onomatopoeias and sound-source images

We propose a method for synthesizing environmental sounds from visually represented onomatopoeias and sound sources. An onomatopoeia is a word that imitates a sound structure, i.e., the text representation of sound. From this perspective, onoma-to-wave has been proposed to synthesize environmental sounds from the desired onomatopoeia texts. Onomatopoeias have another representation: visual-text representations of sounds in comics, advertisements, and virtual reality. A visual onomatopoeia (visual text of onomatopoeia) contains rich information that is not present in the text, such as a long-short duration of the image, so the use of this representation is expected to synthesize diverse sounds. Therefore, we propose visual onoma-to-wave for environmental sound synthesis from visual onomatopoeia. The method can transfer visual concepts of the visual text and sound-source image to the synthesized sound. We also propose a data augmentation method focusing on the repetition of onomatopoeias to enhance the performance of our method. An experimental evaluation shows that the methods can synthesize diverse environmental sounds from visual text and sound-source images.

cs.SD↗

arXiv2022

How Should We Evaluate Synthesized Environmental Sounds

Although several methods of environmental sound synthesis have been proposed, there has been no discussion on how synthesized environmental sounds should be evaluated. Only either subjective or objective evaluations have been conducted in conventional evaluations, and it is not clear what type of evaluation should be carried out. In this paper, we investigate how to evaluate synthesized environmental sounds. We also propose a subjective evaluation methodology to evaluate whether the synthesized sound appropriately represents the information input to the environmental sound synthesis system. In our experiments, we compare the proposed and conventional evaluation methods and show that the results of subjective evaluations tended to differ from those of objective evaluations. From these results, we conclude that it is necessary to conduct not only objective evaluation but also subjective evaluation.

cs.SD↗

arXiv2022

Sound Event Detection Guided by Semantic Contexts of Scenes

Some studies have revealed that contexts of scenes (e.g., "home," "office," and "cooking") are advantageous for sound event detection (SED). Mobile devices and sensing technologies give useful information on scenes for SED without the use of acoustic signals. However, conventional methods can employ pre-defined contexts in inference stages but not undefined contexts. This is because one-hot representations of pre-defined scenes are exploited as prior contexts for such conventional methods. To alleviate this problem, we propose scene-informed SED where pre-defined scene-agnostic contexts are available for more accurate SED. In the proposed method, pre-trained large-scale language models are utilized, which enables SED models to employ unseen semantic contexts of scenes in inference stages. Moreover, we investigated the extent to which the semantic representation of scene contexts is useful for SED. Experimental results performed with TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016/2017 datasets show that the proposed method improves micro and macro F-scores by 4.34 and 3.13 percentage points compared with conventional Conformer- and CNN--BiGRU-based SED, respectively.

cs.SD↗

arXiv2022

Environmental Sound Extraction Using Onomatopoeic Words

An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We propose an environmental-sound-extraction method using onomatopoeic words to specify the target sound to be extracted. By this method, we estimate a time-frequency mask from an input mixture spectrogram and an onomatopoeic word using a U-Net architecture, then extract the corresponding target sound by masking the spectrogram. Experimental results indicate that the proposed method can extract only the target sound corresponding to the onomatopoeic word and performs better than conventional methods that use sound-event classes to specify the target sound.

cs.SD↗

arXiv2022

Onoma-to-wave: Environmental sound synthesis from onomatopoeic words

In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words. As one way of expressing an environmental sound, we can use an onomatopoeic word, which is a character sequence for phonetically imitating a sound. An onomatopoeic word is effective for describing diverse sound features. Therefore, using onomatopoeic words for environmental sound synthesis will enable us to generate diverse environmental sounds. To generate diverse sounds, we propose a method based on a sequence-to-sequence framework for synthesizing environmental sounds from onomatopoeic words. We also propose a method of environmental sound synthesis using onomatopoeic words and sound event labels. The use of sound event labels in addition to onomatopoeic words enables us to capture each sound event's feature depending on the input sound event label. Our subjective experiments show that our proposed methods achieve higher diversity and naturalness than conventional methods using sound event labels.

cs.SD↗

arXiv2021

Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events

In conventional sound event detection (SED) models, two types of events, namely, those that are present and those that do not occur in an acoustic scene, are regarded as the same type of events. The conventional SED methods cannot effectively exploit the difference between the two types of events. All time frames of sound events that do not occur in an acoustic scene are easily regarded as inactive in the scene, that is, the events are easy-to-train. The time frames of the events that are present in a scene must be classified as active in addition to inactive in the acoustic scene, that is, the events are difficult-to-train. To take advantage of the training difficulty, we apply curriculum learning into SED, where models are trained from easy- to difficult-to-train events. To utilize the curriculum learning, we propose a new objective function for SED, wherein the events are trained from easy- to difficult-to-train events. Experimental results show that the F-score of the proposed method is improved by 10.09 percentage points compared with that of the conventional binary cross entropy-based SED.

cs.SD↗