Yoichi Yamashita

Ryotaro Nagase, Ryoichi Takashima, Yoichi Yamashita

Speech emotion recognition (SER) has traditionally relied on categorical or dimensional labels. However, this technique is limited in representing both the diversity and interpretability of emotions. To overcome this limitation, we focus on color attributes, such as hue, saturation, and value, to represent emotions as continuous and interpretable scores. We annotated an emotional speech corpus with color attributes via crowdsourcing and analyzed them. Moreover, we built regression models for color attributes in SER using machine learning and deep learning, and explored the multitask learning of color attribute regression and emotion classification. As a result, we demonstrated the relationship between color attributes and emotions in speech, and successfully developed color attribute regression models for SER. We also showed that multitask learning improved the performance of each task.

Noriyuki Tonami, Keisuke Imoto, Ryotaro Nagase et al.

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.

Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi et al.

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.

Noriyuki Tonami, Keisuke Imoto, Yuki Okamoto et al.

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.

Noriyuki Tonami, Keisuke Imoto, Ryosuke Yamanishi et al.

Sound event detection (SED) and acoustic scene classification (ASC) are important research topics in environmental sound analysis. Many research groups have addressed SED and ASC using neural-network-based methods, such as the convolutional neural network (CNN), recurrent neural network (RNN), and convolutional recurrent neural network (CRNN). The conventional methods address SED and ASC separately even though sound events and acoustic scenes are closely related to each other. For example, in the acoustic scene "office," the sound events "mouse clicking" and "keyboard typing" are likely to occur. Therefore, it is expected that information on sound events and acoustic scenes will be of mutual aid for SED and ASC. In this paper, we propose multitask learning for joint analysis of sound events and acoustic scenes, in which the parts of the networks holding information on sound events and acoustic scenes in common are shared. Experimental results obtained using the TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets indicate that the proposed method improves the performance of SED and ASC by 1.31 and 1.80 percentage points in terms of the F-score, respectively, compared with the conventional CRNN-based method.

Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi et al.

Environmental sound synthesis is a technique for generating a natural environmental sound. Conventional work on environmental sound synthesis using sound event labels cannot finely control synthesized sounds, for example, the pitch and timbre. We consider that onomatopoeic words can be used for environmental sound synthesis. Onomatopoeic words are effective for explaining the feature of sounds. We believe that using onomatopoeic words will enable us to control the fine time-frequency structure of synthesized sounds. However, there is no dataset available for environmental sound synthesis using onomatopoeic words. In this paper, we thus present RWCP-SSD-Onomatopoeia, a dataset consisting of 155,568 onomatopoeic words paired with audio samples for environmental sound synthesis. We also collected self-reported confidence scores and others-reported acceptance scores of onomatopoeic words, to help us investigate the difficulty in the transcription and selection of a suitable word for environmental sound synthesis.

Daichi Akiyama, Keisuke Imoto, Noriyuki Tonami et al.

Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound events vary greatly depending on the sound event class, e.g., the sound event ``fan'' has a long time duration, while the sound event ``mouse clicking'' is instantaneous. The difference in the time duration between sound event classes thus causes a serious data imbalance problem in SED. In this paper, we propose a method for SED using a duration robust loss function, which can focus model training on sound events of short duration. In the proposed method, we focus on a relationship between the duration of the sound event and the ease/difficulty of model training. In particular, many sound events of long duration (e.g., sound event ``fan'') are stationary sounds, which have less variation in their acoustic features and their model training is easy. Meanwhile, some sound events of short duration (e.g., sound event ``object impact'') have more than one audio pattern, such as attack, decay, and release parts. We thus apply a class-wise reweighting to the binary-cross entropy loss function depending on the ease/difficulty of model training. Evaluation experiments conducted using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets show that the proposed method respectively improves the detection performance of sound events by 3.15 and 4.37 percentage points in macro- and micro-Fscores compared with a conventional method using the binary-cross entropy loss function.

Keisuke Imoto, Noriyuki Tonami, Yuma Koizumi et al.

Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to each other, some works have addressed joint analyses of sound events and acoustic scenes based on multitask learning (MTL), in which the knowledge of sound events and scenes can help in estimating them mutually. The conventional MTL-based methods utilize one-hot scene labels to train the relationship between sound events and scenes; thus, the conventional methods cannot model the extent to which sound events and scenes are related. However, in the real environment, common sound events may occur in some acoustic scenes; on the other hand, some sound events occur only in a limited acoustic scene. In this paper, we thus propose a new method for SED based on MTL of SED and ASC using the soft labels of acoustic scenes, which enable us to model the extent to which sound events and scenes are related. Experiments conducted using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets show that the proposed method improves the SED performance by 3.80% in F-score compared with conventional MTL-based SED.

Yuki Okamoto, Keisuke Imoto, Tatsuya Komatsu et al.

Synthesizing and converting environmental sounds have the potential for many applications such as supporting movie and game production, data augmentation for sound event detection and scene classification. Conventional works on synthesizing and converting environmental sounds are based on a physical modeling or concatenative approach. However, there are a limited number of works that have addressed environmental sound synthesis and conversion with statistical generative models; thus, this research area is not yet well organized. In this paper, we review problem definitions, applications, and evaluation methods of environmental sound synthesis and conversion. We then report on environmental sound synthesis using sound event labels, in which we focus on the current performance of statistical environmental sound synthesis and investigate how we should conduct subjective experiments on environmental sound synthesis.

Noriyuki Tonami, Keisuke Imoto, Masahiro Niitsuma et al.

Acoustic event detection and scene classification are major research tasks in environmental sound analysis, and many methods based on neural networks have been proposed. Conventional methods have addressed these tasks separately; however, acoustic events and scenes are closely related to each other. For example, in the acoustic scene `office', the acoustic events `mouse clicking' and `keyboard typing' are likely to occur. In this paper, we propose multitask learning for joint analysis of acoustic events and scenes, which shares the parts of the networks holding information on acoustic events and scenes in common. By integrating the two networks, we expect that information on acoustic scenes will improve the performance of acoustic event detection. Experimental results obtained using TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets indicate that the proposed method improves the performance of acoustic event detection by 10.66 percentage points in terms of the F-score, compared with a conventional method based on a convolutional recurrent neural network.