JP7094340B2 - 畳み込みニューラルネットワークに基づく電話音声信号の強調のための方法 - Google Patents
畳み込みニューラルネットワークに基づく電話音声信号の強調のための方法 Download PDFInfo
- Publication number
- JP7094340B2 JP7094340B2 JP2020174530A JP2020174530A JP7094340B2 JP 7094340 B2 JP7094340 B2 JP 7094340B2 JP 2020174530 A JP2020174530 A JP 2020174530A JP 2020174530 A JP2020174530 A JP 2020174530A JP 7094340 B2 JP7094340 B2 JP 7094340B2
- Authority
- JP
- Japan
- Prior art keywords
- spectral
- convolutional neural
- signal
- voice
- amplitude
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims description 77
- 238000013527 convolutional neural network Methods 0.000 title claims description 30
- 230000005236 sound signal Effects 0.000 title claims description 28
- 230000002708 enhancing effect Effects 0.000 title description 8
- 230000003595 spectral effect Effects 0.000 claims description 37
- 230000009467 reduction Effects 0.000 claims description 16
- 238000001228 spectrum Methods 0.000 claims description 15
- 238000007781 pre-processing Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000012805 post-processing Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 4
- 230000002123 temporal effect Effects 0.000 claims description 4
- 230000001364 causal effect Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 description 16
- 230000006870 function Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 8
- 238000013528 artificial neural network Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000000875 corresponding effect Effects 0.000 description 4
- 230000005534 acoustic noise Effects 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 238000003780 insertion Methods 0.000 description 3
- 230000037431 insertion Effects 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 208000032041 Hearing impaired Diseases 0.000 description 1
- 238000012896 Statistical algorithm Methods 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000002542 deteriorative effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000001747 exhibiting effect Effects 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 239000007943 implant Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000001902 propagating effect Effects 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
- G10L21/0232—Processing in the frequency domain
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/16—Speech classification or search using artificial neural networks
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/21—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being power information
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
- G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/60—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for measuring the quality of voice signals
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/78—Detection of presence or absence of voice signals
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Quality & Reliability (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
Description
電話音声信号のスペクトル表現の振幅及び位相を抽出することを含む前処理段階;
電話音声信号のスペクトル表現の振幅に以下のステップを適用することを含む雑音低減段階:
スペクトル推定を適用する;
知覚的表現(perceptual representation)を計算する;
畳み込みニューラルネットワークを適用し、スペクトル推定値と知覚的表現とに対応した入力を用いて、周波数に依存して時間的に変化する行列/ベクトルからなるウィーナーゲイン推定値を出力として生成する;
次の関数(関数f1)の強調フィルタのゲイン内でウィーナーゲイン推定値を使用する:
前項目で定義した関数(関数f1)を音声強調フィルタとして適用する。
雑音低減段階において強調された振幅と初期位相をマージすることを含む後処理段階。
FB:フィルタバンク
MFCC:メル周波数ケプストラム係数
DNN:深層ニューラルネットワーク
CNN:畳み込みニューラルネットワーク
MMSE:最小平均二乗誤差
SNR:信号対雑音比
POLQA:受聴品質の知覚的客観的分析
Claims (9)
- 畳み込みニューラルネットワークに基づいて電話音声信号を強調するための方法であって、
前記電話音声信号のスペクトル表現の振幅及び位相を抽出することを含む前処理段階(A);
前記電話音声信号のスペクトル表現の振幅に以下のステップを適用することを含む雑音低減段階(B):
スペクトル推定(15)を適用するステップ;
知覚的表現(16、17)を計算するステップ;
畳み込みニューラルネットワーク(18)を適用し、スペクトル推定値(15)と前記知覚的表現(16、17)とに対応した入力を用いて、周波数に依存して時間的に変化する行列/ベクトルからなるウィーナーゲイン推定値(19)を出力として生成するステップ;
次式の関数f1の強調フィルタ内で前記ウィーナーゲイン推定値を使用するステップ:
及び前記ウィーナーゲイン推定値を音声存在確率(21)として使用する;
前記関数f1を音声強調フィルタとして適用するステップ;
前記雑音低減段階(B)で強調された振幅と初期位相をマージすることを含む後処理段階(C)
を含む、方法。 - 前記電話音声信号のスペクトル表現の振幅及び位相を抽出するための前記前処理段階(A)は、音声信号を数十ミリ秒の重なり合うセグメントに分割し、ハミング窓またはハニング窓を適用した後、フーリエ変換を行うことをさらに含む
請求項1に記載の方法。 - 前記スペクトル推定値は、ウェルチ法を用いて計算される
請求項1に記載の方法。 - 前記知覚的表現は、メル尺度フィルタバンク(16)を適用することにより計算される
請求項1に記載の方法。 - 前記知覚的表現は、メル周波数ケプストラム係数(MFCC)(17)により行われる
請求項1に記載の方法。 - 前記前処理段階(A)において得られた位相と前記雑音低減段階(B)において得られたスペクトル振幅とをマージするための前記後処理段階(C)は、逆フーリエ変換を適用し、その後、音声信号の時間的再構成アルゴリズムを適用することをさらに含む
請求項1又は6に記載の方法。 - 前記畳み込みニューラルネットワーク(18)は、因果的であって且つ低遅延性を有する少なくとも1つの畳み込み層を備える
請求項2に記載の方法。 - 前記前処理段階(A)は、SNR、歪み、及びPOLQAから選択された音響品質尺度を用いて、音声信号の品質を客観的に評価すること(2)をさらに含む
請求項1に記載の方法。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP20382110.3 | 2020-02-14 | ||
EP20382110.3A EP3866165B1 (en) | 2020-02-14 | 2020-02-14 | Method for enhancing telephone speech signals based on convolutional neural networks |
Publications (2)
Publication Number | Publication Date |
---|---|
JP2021128328A JP2021128328A (ja) | 2021-09-02 |
JP7094340B2 true JP7094340B2 (ja) | 2022-07-01 |
Family
ID=69810751
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2020174530A Active JP7094340B2 (ja) | 2020-02-14 | 2020-10-16 | 畳み込みニューラルネットワークに基づく電話音声信号の強調のための方法 |
Country Status (4)
Country | Link |
---|---|
US (1) | US11804234B2 (ja) |
EP (1) | EP3866165B1 (ja) |
JP (1) | JP7094340B2 (ja) |
ES (1) | ES2928295T3 (ja) |
Families Citing this family (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111445919B (zh) * | 2020-03-13 | 2023-01-20 | 紫光展锐(重庆)科技有限公司 | 结合ai模型的语音增强方法、***、电子设备和介质 |
US20210350819A1 (en) * | 2020-05-07 | 2021-11-11 | Netflix, Inc. | Techniques for training a multitask learning model to assess perceived audio quality |
US20210012767A1 (en) * | 2020-09-25 | 2021-01-14 | Intel Corporation | Real-time dynamic noise reduction using convolutional networks |
US11978466B2 (en) * | 2021-06-02 | 2024-05-07 | Arizona Board Of Regents On Behalf Of Arizona State University | Systems, methods, and apparatuses for restoring degraded speech via a modified diffusion model |
CN113782011B (zh) * | 2021-08-26 | 2024-04-09 | 清华大学苏州汽车研究院(相城) | 频带增益模型的训练方法及用于车载场景的语音降噪方法 |
CN115002743A (zh) * | 2022-06-28 | 2022-09-02 | 广西东信易通科技有限公司 | 一种基于机器学习算法的中间号场景通话增强*** |
CN115497496B (zh) * | 2022-09-22 | 2023-11-14 | 东南大学 | 一种基于FirePS卷积神经网络的语音增强方法 |
CN115376501B (zh) * | 2022-10-26 | 2023-02-14 | 深圳市北科瑞讯信息技术有限公司 | 语音增强方法及装置、存储介质、电子设备 |
CN115810364B (zh) * | 2023-02-07 | 2023-04-28 | 海纳科德(湖北)科技有限公司 | 混音环境中的端到端目标声信号提取方法及*** |
CN115856987B (zh) * | 2023-02-28 | 2023-05-02 | 西南科技大学 | 一种复杂环境下的核脉冲信号与噪声信号甄别方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2002517021A (ja) | 1998-05-27 | 2002-06-11 | テレフオンアクチーボラゲット エル エム エリクソン(パブル) | 線形たたみ込みおよび因果的フィルタリングを用いたスペクトルサブトラクションによる信号雑音低減 |
JP2018028580A (ja) | 2016-08-16 | 2018-02-22 | 日本電信電話株式会社 | 音源強調学習装置、音源強調装置、音源強調学習方法、プログラム |
JP2019531494A (ja) | 2016-10-12 | 2019-10-31 | アイフライテック カンパニー,リミテッド | 音声品質評価方法及び装置 |
JP2021516786A (ja) | 2018-08-09 | 2021-07-08 | ▲騰▼▲訊▼科技(深▲セン▼)有限公司 | 複数人の音声を分離する方法、装置、およびコンピュータプログラム |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10812915B2 (en) * | 2017-10-27 | 2020-10-20 | Starkey Laboratories, Inc. | Electronic device using a compound metric for sound enhancement |
US10672414B2 (en) * | 2018-04-13 | 2020-06-02 | Microsoft Technology Licensing, Llc | Systems, methods, and computer-readable media for improved real-time audio processing |
US10741192B2 (en) * | 2018-05-07 | 2020-08-11 | Qualcomm Incorporated | Split-domain speech signal enhancement |
US11456007B2 (en) * | 2019-01-11 | 2022-09-27 | Samsung Electronics Co., Ltd | End-to-end multi-task denoising for joint signal distortion ratio (SDR) and perceptual evaluation of speech quality (PESQ) optimization |
-
2020
- 2020-02-14 ES ES20382110T patent/ES2928295T3/es active Active
- 2020-02-14 EP EP20382110.3A patent/EP3866165B1/en active Active
- 2020-10-16 JP JP2020174530A patent/JP7094340B2/ja active Active
- 2020-12-17 US US17/124,794 patent/US11804234B2/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2002517021A (ja) | 1998-05-27 | 2002-06-11 | テレフオンアクチーボラゲット エル エム エリクソン(パブル) | 線形たたみ込みおよび因果的フィルタリングを用いたスペクトルサブトラクションによる信号雑音低減 |
JP2018028580A (ja) | 2016-08-16 | 2018-02-22 | 日本電信電話株式会社 | 音源強調学習装置、音源強調装置、音源強調学習方法、プログラム |
JP2019531494A (ja) | 2016-10-12 | 2019-10-31 | アイフライテック カンパニー,リミテッド | 音声品質評価方法及び装置 |
JP2021516786A (ja) | 2018-08-09 | 2021-07-08 | ▲騰▼▲訊▼科技(深▲セン▼)有限公司 | 複数人の音声を分離する方法、装置、およびコンピュータプログラム |
Non-Patent Citations (2)
Title |
---|
Israel Cohen, Baruch Berdugo,Speech enhancement for non-stationary noise environments,Signal Processing,Volume 81, Issue 11,2001年06月26日,pp.2403-2418 |
Yang Xiang, Changchun Bao,Speech Enhancement Based on Cepstral Mapping and Deep Neural Networks,2018 IEEE 4th International Conference on Computer and Communications,2018年12月10日,pp.1263-1267 |
Also Published As
Publication number | Publication date |
---|---|
US20210256988A1 (en) | 2021-08-19 |
EP3866165B1 (en) | 2022-08-17 |
JP2021128328A (ja) | 2021-09-02 |
US11804234B2 (en) | 2023-10-31 |
ES2928295T3 (es) | 2022-11-16 |
EP3866165A1 (en) | 2021-08-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
JP7094340B2 (ja) | 畳み込みニューラルネットワークに基づく電話音声信号の強調のための方法 | |
Luo et al. | Real-time single-channel dereverberation and separation with time-domain audio separation network. | |
Mirsamadi et al. | Causal speech enhancement combining data-driven learning and suppression rule estimation. | |
Tu et al. | A hybrid approach to combining conventional and deep learning techniques for single-channel speech enhancement and recognition | |
Swami et al. | Speech enhancement by noise driven adaptation of perceptual scales and thresholds of continuous wavelet transform coefficients | |
Schröter et al. | Low latency speech enhancement for hearing aids using deep filtering | |
Farias et al. | Blind adaptive mask to improve intelligibility of non-stationary noisy speech | |
Garg | Speech enhancement using long short term memory with trained speech features and adaptive wiener filter | |
Vanjari et al. | Comparative Analysis of Speech Enhancement Techniques in Perceptive of Hearing Aid Design | |
Saleem | Single channel noise reduction system in low SNR | |
Nossier et al. | Two-stage deep learning approach for speech enhancement and reconstruction in the frequency and time domains | |
Rani et al. | Significance of phase in DNN based speech enhancement algorithms | |
Chhetri et al. | Speech Enhancement: A Survey of Approaches and Applications | |
Roy et al. | Deep residual network-based augmented Kalman filter for speech enhancement | |
Sivapatham et al. | Gammatone filter bank-deep neural network-based monaural speech enhancement for unseen conditions | |
Kim et al. | iDeepMMSE: An improved deep learning approach to MMSE speech and noise power spectrum estimation for speech enhancement. | |
Feng et al. | DNN-based linear prediction residual enhancement for speech dereverberation | |
Naithani et al. | Subjective evaluation of deep neural network based speech enhancement systems in real-world conditions | |
Gui et al. | Adaptive subband Wiener filtering for speech enhancement using critical-band gammatone filterbank | |
Prasad et al. | Two microphone technique to improve the speech intelligibility under noisy environment | |
Roy et al. | Causal convolutional neural network-based Kalman filter for speech enhancement | |
KR102688359B1 (ko) | 음성 및 잡음 파워 스펙트럼 추정을 이용한 음성 향상 장치 및 방법 | |
Babu et al. | Modified Kalman Filter-based Approach in Comparison with Traditional Speech Enhancement Algorithms from Adverse Noisy Environments | |
Mellahi et al. | Notice of Violation of IEEE Publication Principles: LPCs enhancement in iterative Kalman filtering for speech enhancement using overlapped frames | |
Rustrana et al. | Spectral Methods for Single Channel Speech Enhancement in Multi-Source Environment |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20210513 |
|
A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20220513 |
|
TRDD | Decision of grant or rejection written | ||
A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20220524 |
|
A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20220621 |
|
R150 | Certificate of patent or registration of utility model |
Ref document number: 7094340 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |