Eurasip Journal on Audio Speech and Music Processing最新文献_第6页

Efficient bandwidth extension of musical signals using a differentiable harmonic plus noise model 利用可微谐波加噪声模型对音乐信号进行有效的带宽扩展

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2023-12-05 DOI: 10.1186/s13636-023-00315-5

Pierre-Amaury Grumiaux, Mathieu Lagrange

{"title":"Efficient bandwidth extension of musical signals using a differentiable harmonic plus noise model","authors":"Pierre-Amaury Grumiaux, Mathieu Lagrange","doi":"10.1186/s13636-023-00315-5","DOIUrl":"https://doi.org/10.1186/s13636-023-00315-5","url":null,"abstract":"The task of bandwidth extension addresses the generation of missing high frequencies of audio signals based on knowledge of the low-frequency part of the sound. This task applies to various problems, such as audio coding or audio restoration. In this article, we focus on efficient bandwidth extension of monophonic and polyphonic musical signals using a differentiable digital signal processing (DDSP) model. Such a model is composed of a neural network part with relatively few parameters trained to infer the parameters of a differentiable digital signal processing model, which efficiently generates the output full-band audio signal. We first address bandwidth extension of monophonic signals, and then propose two methods to explicitly handle polyphonic signals. The benefits of the proposed models are first demonstrated on monophonic and polyphonic synthetic data against a baseline and a deep-learning-based ResNet model. The models are next evaluated on recorded monophonic and polyphonic data, for a wide variety of instruments and musical genres. We show that all proposed models surpass a higher complexity deep learning model for an objective metric computed in the frequency domain. A MUSHRA listening test confirms the superiority of the proposed approach in terms of perceptual quality.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":" 6","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138492481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Piano score rearrangement into multiple difficulty levels via notation-to-notation approach 钢琴乐谱重新排列成多个难度级别，通过符号到符号的方法

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2023-12-05 DOI: 10.1186/s13636-023-00321-7

Masahiro Suzuki

{"title":"Piano score rearrangement into multiple difficulty levels via notation-to-notation approach","authors":"Masahiro Suzuki","doi":"10.1186/s13636-023-00321-7","DOIUrl":"https://doi.org/10.1186/s13636-023-00321-7","url":null,"abstract":"Musical score rearrangement is an emerging area in symbolic music processing, which aims to transform a musical score into a different style. This study focuses on the task of changing the playing difficulty of piano scores, addressing two challenges in musical score rearrangement. First, we address the challenge of handling musical notation on scores. While symbolic music research often relies on note-level (MIDI-equivalent) information, musical scores contain notation that cannot be adequately represented at the note level. We propose an end-to-end framework that utilizes tokenized representations of notation to directly rearrange musical scores at the notation level. We also propose the ST+ representation, which includes a novel structure and token types for better score rearrangement. Second, we address the challenge of rearranging musical scores across multiple difficulty levels. We introduce a difficulty conditioning scheme to train a single sequence model capable of handling various difficulty levels, while leveraging scores from various levels in model training. We collect commercial-quality pop piano scores at four difficulty levels and train a MEGA model (with 0.3M parameters) to rearrange between these levels. Objective evaluation shows that our method successfully rearranges piano scores into other three difficulty levels, achieving comparable difficulty to human-made scores. Additionally, our method successfully generates musical notation including articulations. Subjective evaluation (by score experts and musicians) also reveals that our generated scores generally surpass the quality of previous rule-based or note-level methods on several criteria. Our framework enables novel notation-to-notation processing of scores and can be applied to various score rearrangement tasks.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":" 5","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138492482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective acoustic parameters for automatic classification of performed and synthesized Guzheng music 古筝演奏与合成音乐自动分类的有效声学参数

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2023-12-01 DOI: 10.1186/s13636-023-00320-8

Huiwen Xue, Chenxin Sun, Mingcheng Tang, Chenrui Hu, Zhengqing Yuan, Min Huang, Zhongzhe Xiao

引用次数: 0

Predominant audio source separation in polyphonic music 在复调音乐中主要的音源分离

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2023-11-24 DOI: 10.1186/s13636-023-00316-4

Lekshmi Chandrika Reghunath, Rajeev Rajan

{"title":"Predominant audio source separation in polyphonic music","authors":"Lekshmi Chandrika Reghunath, Rajeev Rajan","doi":"10.1186/s13636-023-00316-4","DOIUrl":"https://doi.org/10.1186/s13636-023-00316-4","url":null,"abstract":"Predominant source separation is the separation of one or more desired predominant signals, such as voice or leading instruments, from polyphonic music. The proposed work uses time-frequency filtering on predominant source separation and conditional adversarial networks to improve the perceived quality of isolated sounds. The pitch tracks corresponding to the prominent sound sources of the polyphonic music are estimated using a predominant pitch extraction algorithm and a binary mask corresponding to each pitch track and its harmonics are generated. Time-frequency filtering is performed on the spectrogram of the input signal using a binary mask that isolates the dominant sources based on pitch. The perceptual quality of source-separated music signal is enhanced using a CycleGAN-based conditional adversarial network operating on spectrogram images. The proposed work is systematically evaluated using the IRMAS and ADC 2004 datasets. Subjective and objective evaluations have been carried out. The reconstructed spectrogram is converted back to music signals by applying the inverse short-time Fourier transform. The intelligibility of separated audio is enhanced using an intelligibility enhancement module based on an audio style transfer scheme. The performance of the proposed method is compared with state-of-the-art Demucs and Wave-U-Net architectures and shows competing performance both objectively and subjectively.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":" 8","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138492479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MYRiAD: a multi-array room acoustic database. MYRiAD:一个多阵列房间声学数据库。

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2023-01-01 DOI: 10.1186/s13636-023-00284-9

Thomas Dietzen, Randall Ali, Maja Taseska, Toon van Waterschoot

{"title":"MYRiAD: a multi-array room acoustic database.","authors":"Thomas Dietzen, Randall Ali, Maja Taseska, Toon van Waterschoot","doi":"10.1186/s13636-023-00284-9","DOIUrl":"https://doi.org/10.1186/s13636-023-00284-9","url":null,"abstract":"In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1 s and 0.5 s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. The database is publicly available (https://zenodo.org/record/7389996).","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":"2023 1","pages":"17"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10133077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9760637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Explicit-memory multiresolution adaptive framework for speech and music separation. 用于语音和音乐分离的显式记忆多分辨率自适应框架。

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2023-01-01 Epub Date: 2023-05-09 DOI: 10.1186/s13636-023-00286-7

Ashwin Bellur, Karan Thakkar, Mounya Elhilali

{"title":"Explicit-memory multiresolution adaptive framework for speech and music separation.","authors":"Ashwin Bellur, Karan Thakkar, Mounya Elhilali","doi":"10.1186/s13636-023-00286-7","DOIUrl":"10.1186/s13636-023-00286-7","url":null,"abstract":"The human auditory system employs a number of principles to facilitate the selection of perceptually separated streams from a complex sound mixture. The brain leverages multi-scale redundant representations of the input and uses memory (or priors) to guide the selection of a target sound from the input mixture. Moreover, feedback mechanisms refine the memory constructs resulting in further improvement of selectivity of a particular sound object amidst dynamic backgrounds. The present study proposes a unified end-to-end computational framework that mimics these principles for sound source separation applied to both speech and music mixtures. While the problems of speech enhancement and music separation have often been tackled separately due to constraints and specificities of each signal domain, the current work posits that common principles for sound source separation are domain-agnostic. In the proposed scheme, parallel and hierarchical convolutional paths map input mixtures onto redundant but distributed higher-dimensional subspaces and utilize the concept of temporal coherence to gate the selection of embeddings belonging to a target stream abstracted in memory. These explicit memories are further refined through self-feedback from incoming observations in order to improve the system's selectivity when faced with unknown backgrounds. The model yields stable outcomes of source separation for both speech and music mixtures and demonstrates benefits of explicit memory as a powerful representation of priors that guide information selection from complex inputs.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":"2023 1","pages":"20"},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10301080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time-frequency scattering accurately models auditory similarities between instrumental playing techniques. 时频散射准确地模拟了乐器演奏技术之间的听觉相似性。

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2021-01-01 Epub Date: 2021-01-11 DOI: 10.1186/s13636-020-00187-z

Vincent Lostanlen, Christian El-Hajj, Mathias Rossignol, Grégoire Lafay, Joakim Andén, Mathieu Lagrange

{"title":"Time-frequency scattering accurately models auditory similarities between instrumental playing techniques.","authors":"Vincent Lostanlen, Christian El-Hajj, Mathias Rossignol, Grégoire Lafay, Joakim Andén, Mathieu Lagrange","doi":"10.1186/s13636-020-00187-z","DOIUrl":"https://doi.org/10.1186/s13636-020-00187-z","url":null,"abstract":"Instrumentalplaying techniques such as vibratos, glissandos, and trills often denote musical expressivity, both in classical and folk contexts. However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called \"ordinary\" technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject. In this article, we ask 31 human participants to organize 78 isolated notes into a set of timbre clusters. Analyzing their responses suggests that timbre perception operates within a more flexible taxonomy than those provided by instruments or playing techniques alone. In addition, we propose a machine listening model to recover the cluster graph of auditory similarities across instruments, mutes, and techniques. Our model relies on joint time-frequency scattering features to extract spectrotemporal modulations as acoustic features. Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we report a state-of-the-art average precision at rank five (AP@5) of 99.0%±1. An ablation study demonstrates that removing either the joint time-frequency scattering transform or the metric learning algorithm noticeably degrades performance.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":"2021 1","pages":"3"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13636-020-00187-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38854143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network. 基于上下文叠加扩展卷积神经网络的端到端语音情感识别。

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2021-01-01 Epub Date: 2021-05-12 DOI: 10.1186/s13636-021-00208-5

Duowei Tang, Peter Kuppens, Luc Geurts, Toon van Waterschoot

{"title":"End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network.","authors":"Duowei Tang, Peter Kuppens, Luc Geurts, Toon van Waterschoot","doi":"10.1186/s13636-021-00208-5","DOIUrl":"https://doi.org/10.1186/s13636-021-00208-5","url":null,"abstract":"Amongst the various characteristics of a speech signal, the expression of emotion is one of the characteristics that exhibits the slowest temporal dynamics. Hence, a performant speech emotion recognition (SER) system requires a predictive model that is capable of learning sufficiently long temporal dependencies in the analysed speech signal. Therefore, in this work, we propose a novel end-to-end neural network architecture based on the concept of dilated causal convolution with context stacking. Firstly, the proposed model consists only of parallelisable layers and is hence suitable for parallel processing, while avoiding the inherent lack of parallelisability occurring with recurrent neural network (RNN) layers. Secondly, the design of a dedicated dilated causal convolution block allows the model to have a receptive field as large as the input sequence length, while maintaining a reasonably low computational cost. Thirdly, by introducing a context stacking structure, the proposed model is capable of exploiting long-term temporal dependencies hence providing an alternative to the use of RNN layers. We evaluate the proposed model in SER regression and classification tasks and provide a comparison with a state-of-the-art end-to-end SER model. Experimental results indicate that the proposed model requires only 1/3 of the number of model parameters used in the state-of-the-art model, while also significantly improving SER performance. Further experiments are reported to understand the impact of using various types of input representations (i.e. raw audio samples vs log mel-spectrograms) and to illustrate the benefits of an end-to-end approach over the use of hand-crafted audio features. Moreover, we show that the proposed model can efficiently learn intermediate embeddings preserving speech emotion information.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":"2021 1","pages":"18"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13636-021-00208-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39683580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Musical note onset detection based on a spectral sparsity measure. 基于频谱稀疏度量的音乐音符起音检测。

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2021-01-01 Epub Date: 2021-07-28 DOI: 10.1186/s13636-021-00214-7

Mina Mounir, Peter Karsmakers, Toon van Waterschoot

{"title":"Musical note onset detection based on a spectral sparsity measure.","authors":"Mina Mounir, Peter Karsmakers, Toon van Waterschoot","doi":"10.1186/s13636-021-00214-7","DOIUrl":"10.1186/s13636-021-00214-7","url":null,"abstract":"If music is the language of the universe, musical note onsets may be the syllables for this language. Not only do note onsets define the temporal pattern of a musical piece, but their time-frequency characteristics also contain rich information about the identity of the musical instrument producing the notes. Note onset detection (NOD) is the basic component for many music information retrieval tasks and has attracted significant interest in audio signal processing research. In this paper, we propose an NOD method based on a novel feature coined as Normalized Identification of Note Onset based on Spectral Sparsity (NINOS2). The NINOS2 feature can be thought of as a spectral sparsity measure, aiming to exploit the difference in spectral sparsity between the different parts of a musical note. This spectral structure is revealed when focusing on low-magnitude spectral components that are traditionally filtered out when computing note onset features. We present an extensive set of NOD simulation results covering a wide range of instruments, playing styles, and mixing options. The proposed algorithm consistently outperforms the baseline Logarithmic Spectral Flux (LSF) feature for the most difficult group of instruments which are the sustained-strings instruments. It also shows better performance for challenging scenarios including polyphonic music and vibrato performances.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":"2021 1","pages":"30"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39683581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep multiple instance learning for foreground speech localization in ambient audio from wearable devices. 基于深度多实例学习的可穿戴设备环境音频前景语音定位。

IF 2.4 3区计算机科学

Eurasip Journal on Audio Speech and Music Processing Pub Date : 2021-01-01 Epub Date: 2021-02-03 DOI: 10.1186/s13636-020-00194-0

Rajat Hebbar, Pavlos Papadopoulos, Ramon Reyes, Alexander F Danvers, Angelina J Polsinelli, Suzanne A Moseley, David A Sbarra, Matthias R Mehl, Shrikanth Narayanan

{"title":"Deep multiple instance learning for foreground speech localization in ambient audio from wearable devices.","authors":"Rajat Hebbar, Pavlos Papadopoulos, Ramon Reyes, Alexander F Danvers, Angelina J Polsinelli, Suzanne A Moseley, David A Sbarra, Matthias R Mehl, Shrikanth Narayanan","doi":"10.1186/s13636-020-00194-0","DOIUrl":"https://doi.org/10.1186/s13636-020-00194-0","url":null,"abstract":"Over the recent years, machine learning techniques have been employed to produce state-of-the-art results in several audio related tasks. The success of these approaches has been largely due to access to large amounts of open-source datasets and enhancement of computational resources. However, a shortcoming of these methods is that they often fail to generalize well to tasks from real life scenarios, due to domain mismatch. One such task is foreground speech detection from wearable audio devices. Several interfering factors such as dynamically varying environmental conditions, including background speakers, TV, or radio audio, render foreground speech detection to be a challenging task. Moreover, obtaining precise moment-to-moment annotations of audio streams for analysis and model training is also time-consuming and costly. In this work, we use multiple instance learning (MIL) to facilitate development of such models using annotations available at a lower time-resolution (coarsely labeled). We show how MIL can be applied to localize foreground speech in coarsely labeled audio and show both bag-level and instance-level results. We also study different pooling methods and how they can be adapted to densely distributed events as observed in our application. Finally, we show improvements using speech activity detection embeddings as features for foreground detection.","PeriodicalId":49202,"journal":{"name":"Eurasip Journal on Audio Speech and Music Processing","volume":"2021 1","pages":"7"},"PeriodicalIF":2.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13636-020-00194-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25367001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9