Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network Article Swipe
Juan Li
,
Xueying Zhang
,
Lixia Huang
,
Fenglian Li
,
Shufei Duan
,
Ying Sun
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12199518
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12199518
In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequency part of speech. In contrast, the inverse Mel (IMel) spectrogram, which focuses on the high-frequency part, is proposed to comprehensively analyze emotions. Because the convolutional neural network-stacked sparse autoencoder (CNN-SSAE) can extract deep optimized features, the Mel-IMel dual-channel complementary structure is proposed. In the first channel, a CNN is used to extract the low-frequency information of the Mel spectrogram. The other channel extracts the high-frequency information of the IMel spectrogram. This information is transmitted into an SSAE to reduce the number of dimensions, and obtain the optimized information. Experimental results show that the highest recognition rates achieved on the EMO-DB, SAVEE, and RAVDESS datasets were 94.79%, 88.96%, and 83.18%, respectively. The conclusions are that the recognition rate of the two spectrograms was higher than that of each of the single spectrograms, which proves that the two spectrograms are complementary. The SSAE followed the CNN to get the optimized information, and the recognition rate was further improved, which proves the effectiveness of the CNN-SSAE network.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app12199518
- https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963
- OA Status
- gold
- Cited By
- 19
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4296994433
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4296994433Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app12199518Digital Object Identifier
- Title
-
Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-22Full publication date if available
- Authors
-
Juan Li, Xueying Zhang, Lixia Huang, Fenglian Li, Shufei Duan, Ying SunList of authors in order
- Landing page
-
https://doi.org/10.3390/app12199518Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963Direct OA link when available
- Concepts
-
Spectrogram, Computer science, Speech recognition, Convolutional neural network, Artificial intelligence, Autoencoder, Channel (broadcasting), Pattern recognition (psychology), Artificial neural network, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 7, 2023: 10, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4296994433 |
|---|---|
| doi | https://doi.org/10.3390/app12199518 |
| ids.doi | https://doi.org/10.3390/app12199518 |
| ids.openalex | https://openalex.org/W4296994433 |
| fwci | 4.67388134 |
| type | article |
| title | Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network |
| biblio.issue | 19 |
| biblio.volume | 12 |
| biblio.last_page | 9518 |
| biblio.first_page | 9518 |
| topics[0].id | https://openalex.org/T10667 |
| topics[0].field.id | https://openalex.org/fields/32 |
| topics[0].field.display_name | Psychology |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3205 |
| topics[0].subfield.display_name | Experimental and Cognitive Psychology |
| topics[0].display_name | Emotion and Mood Recognition |
| topics[1].id | https://openalex.org/T10860 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.991100013256073 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1711 |
| topics[1].subfield.display_name | Signal Processing |
| topics[1].display_name | Speech and Audio Processing |
| topics[2].id | https://openalex.org/T10429 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9901999831199646 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | EEG and Brain-Computer Interfaces |
| is_xpac | False |
| apc_list.value | 2300 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2300 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2490 |
| concepts[0].id | https://openalex.org/C45273575 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9805585145950317 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q578970 |
| concepts[0].display_name | Spectrogram |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7387158274650574 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C28490314 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6418909430503845 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[2].display_name | Speech recognition |
| concepts[3].id | https://openalex.org/C81363708 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6299160122871399 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[3].display_name | Convolutional neural network |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5586051940917969 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C101738243 |
| concepts[5].level | 3 |
| concepts[5].score | 0.548212468624115 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q786435 |
| concepts[5].display_name | Autoencoder |
| concepts[6].id | https://openalex.org/C127162648 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5234289169311523 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q16858953 |
| concepts[6].display_name | Channel (broadcasting) |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46256622672080994 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C50644808 |
| concepts[8].level | 2 |
| concepts[8].score | 0.3365974426269531 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[8].display_name | Artificial neural network |
| concepts[9].id | https://openalex.org/C76155785 |
| concepts[9].level | 1 |
| concepts[9].score | 0.13302889466285706 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[9].display_name | Telecommunications |
| keywords[0].id | https://openalex.org/keywords/spectrogram |
| keywords[0].score | 0.9805585145950317 |
| keywords[0].display_name | Spectrogram |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7387158274650574 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/speech-recognition |
| keywords[2].score | 0.6418909430503845 |
| keywords[2].display_name | Speech recognition |
| keywords[3].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[3].score | 0.6299160122871399 |
| keywords[3].display_name | Convolutional neural network |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5586051940917969 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/autoencoder |
| keywords[5].score | 0.548212468624115 |
| keywords[5].display_name | Autoencoder |
| keywords[6].id | https://openalex.org/keywords/channel |
| keywords[6].score | 0.5234289169311523 |
| keywords[6].display_name | Channel (broadcasting) |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.46256622672080994 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[8].score | 0.3365974426269531 |
| keywords[8].display_name | Artificial neural network |
| keywords[9].id | https://openalex.org/keywords/telecommunications |
| keywords[9].score | 0.13302889466285706 |
| keywords[9].display_name | Telecommunications |
| language | en |
| locations[0].id | doi:10.3390/app12199518 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210205812 |
| locations[0].source.issn | 2076-3417 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2076-3417 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Applied Sciences |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Applied Sciences |
| locations[0].landing_page_url | https://doi.org/10.3390/app12199518 |
| locations[1].id | pmh:oai:doaj.org/article:a7e135e78a3f46e5b73fd1f8e1371c63 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Applied Sciences, Vol 12, Iss 19, p 9518 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/a7e135e78a3f46e5b73fd1f8e1371c63 |
| locations[2].id | pmh:oai:mdpi.com:/2076-3417/12/19/9518/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Applied Sciences; Volume 12; Issue 19; Pages: 9518 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/app12199518 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5100421080 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4110-3295 |
| authorships[0].author.display_name | Juan Li |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I9086337 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I110262843 |
| authorships[0].affiliations[1].raw_affiliation_string | Department of Physics and Electronic Engineering, Yuncheng University, Yuncheng 044000, China |
| authorships[0].institutions[0].id | https://openalex.org/I9086337 |
| authorships[0].institutions[0].ror | https://ror.org/03kv08d37 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I9086337 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Taiyuan University of Technology |
| authorships[0].institutions[1].id | https://openalex.org/I110262843 |
| authorships[0].institutions[1].ror | https://ror.org/03qt1g669 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I110262843 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Yuncheng University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Juan Li |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China, Department of Physics and Electronic Engineering, Yuncheng University, Yuncheng 044000, China |
| authorships[1].author.id | https://openalex.org/A5100445969 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2035-0329 |
| authorships[1].author.display_name | Xueying Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I9086337 |
| authorships[1].affiliations[0].raw_affiliation_string | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[1].institutions[0].id | https://openalex.org/I9086337 |
| authorships[1].institutions[0].ror | https://ror.org/03kv08d37 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I9086337 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Taiyuan University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xueying Zhang |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[2].author.id | https://openalex.org/A5109489632 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Lixia Huang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I9086337 |
| authorships[2].affiliations[0].raw_affiliation_string | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[2].institutions[0].id | https://openalex.org/I9086337 |
| authorships[2].institutions[0].ror | https://ror.org/03kv08d37 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I9086337 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Taiyuan University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lixia Huang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[3].author.id | https://openalex.org/A5056376704 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Fenglian Li |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I9086337 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[3].institutions[0].id | https://openalex.org/I9086337 |
| authorships[3].institutions[0].ror | https://ror.org/03kv08d37 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I9086337 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Taiyuan University of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Fenglian Li |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[4].author.id | https://openalex.org/A5073037697 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6072-8237 |
| authorships[4].author.display_name | Shufei Duan |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I9086337 |
| authorships[4].affiliations[0].raw_affiliation_string | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[4].institutions[0].id | https://openalex.org/I9086337 |
| authorships[4].institutions[0].ror | https://ror.org/03kv08d37 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I9086337 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Taiyuan University of Technology |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Shufei Duan |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[5].author.id | https://openalex.org/A5031275848 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5432-4620 |
| authorships[5].author.display_name | Ying Sun |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I9086337 |
| authorships[5].affiliations[0].raw_affiliation_string | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| authorships[5].institutions[0].id | https://openalex.org/I9086337 |
| authorships[5].institutions[0].ror | https://ror.org/03kv08d37 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I9086337 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Taiyuan University of Technology |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Ying Sun |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-09-25T00:00:00 |
| display_name | Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10667 |
| primary_topic.field.id | https://openalex.org/fields/32 |
| primary_topic.field.display_name | Psychology |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3205 |
| primary_topic.subfield.display_name | Experimental and Cognitive Psychology |
| primary_topic.display_name | Emotion and Mood Recognition |
| related_works | https://openalex.org/W2530685530, https://openalex.org/W3013693939, https://openalex.org/W4375868962, https://openalex.org/W2011227383, https://openalex.org/W2566616303, https://openalex.org/W2088854863, https://openalex.org/W2159052453, https://openalex.org/W3131327266, https://openalex.org/W2897924318, https://openalex.org/W2138997758 |
| cited_by_count | 19 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 10 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/app12199518 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210205812 |
| best_oa_location.source.issn | 2076-3417 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2076-3417 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Applied Sciences |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Applied Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.3390/app12199518 |
| primary_location.id | doi:10.3390/app12199518 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210205812 |
| primary_location.source.issn | 2076-3417 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2076-3417 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Applied Sciences |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2076-3417/12/19/9518/pdf?version=1663918963 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.3390/app12199518 |
| publication_date | 2022-09-22 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3097274210, https://openalex.org/W3113497772, https://openalex.org/W2748543394, https://openalex.org/W2071954570, https://openalex.org/W2963631961, https://openalex.org/W3177392932, https://openalex.org/W3126625480, https://openalex.org/W3210677206, https://openalex.org/W2074788634, https://openalex.org/W2997399314, https://openalex.org/W4286544676, https://openalex.org/W3130521866, https://openalex.org/W2184242386, https://openalex.org/W2959133507, https://openalex.org/W3094312606, https://openalex.org/W2969889150, https://openalex.org/W2972273766, https://openalex.org/W2766272105, https://openalex.org/W3161428216, https://openalex.org/W3020839184, https://openalex.org/W2793023716, https://openalex.org/W3129285739, https://openalex.org/W2747664154, https://openalex.org/W3013363049, https://openalex.org/W2981559298, https://openalex.org/W2962686539, https://openalex.org/W2087618018, https://openalex.org/W3145643603, https://openalex.org/W2111926505, https://openalex.org/W2792311129, https://openalex.org/W2974743569, https://openalex.org/W2767812403, https://openalex.org/W2092489135, https://openalex.org/W2092718714, https://openalex.org/W2766756589, https://openalex.org/W3084484668, https://openalex.org/W3088789516, https://openalex.org/W2885005742, https://openalex.org/W3136465594, https://openalex.org/W2944458161, https://openalex.org/W3019352575, https://openalex.org/W2085662862, https://openalex.org/W2888650348, https://openalex.org/W2799331981, https://openalex.org/W2534377085, https://openalex.org/W3019821141, https://openalex.org/W2248620004, https://openalex.org/W2769541277, https://openalex.org/W2950393176, https://openalex.org/W175750906, https://openalex.org/W2803193013, https://openalex.org/W2595557705, https://openalex.org/W3006705189 |
| referenced_works_count | 53 |
| abstract_inverted_index.a | 25, 83 |
| abstract_inverted_index.In | 0, 40, 79 |
| abstract_inverted_index.an | 112 |
| abstract_inverted_index.by | 20 |
| abstract_inverted_index.in | 29 |
| abstract_inverted_index.is | 24, 53, 77, 85, 109 |
| abstract_inverted_index.of | 3, 8, 38, 92, 103, 118, 154, 162, 164, 197 |
| abstract_inverted_index.on | 34, 49, 134 |
| abstract_inverted_index.to | 55, 87, 114, 181 |
| abstract_inverted_index.CNN | 84, 180 |
| abstract_inverted_index.Mel | 22, 44, 94 |
| abstract_inverted_index.The | 96, 147, 176 |
| abstract_inverted_index.and | 13, 120, 138, 144, 186 |
| abstract_inverted_index.are | 149, 174 |
| abstract_inverted_index.can | 67 |
| abstract_inverted_index.get | 182 |
| abstract_inverted_index.has | 15 |
| abstract_inverted_index.the | 1, 6, 17, 35, 42, 50, 60, 72, 80, 89, 93, 100, 104, 116, 122, 129, 135, 151, 155, 165, 171, 179, 183, 187, 195, 198 |
| abstract_inverted_index.two | 156, 172 |
| abstract_inverted_index.was | 158, 190 |
| abstract_inverted_index.IMel | 105 |
| abstract_inverted_index.SSAE | 113, 177 |
| abstract_inverted_index.This | 107 |
| abstract_inverted_index.deep | 69 |
| abstract_inverted_index.each | 163 |
| abstract_inverted_index.goal | 18 |
| abstract_inverted_index.into | 111 |
| abstract_inverted_index.part | 37 |
| abstract_inverted_index.rate | 153, 189 |
| abstract_inverted_index.show | 127 |
| abstract_inverted_index.than | 160 |
| abstract_inverted_index.that | 128, 150, 161, 170 |
| abstract_inverted_index.used | 28, 86 |
| abstract_inverted_index.were | 141 |
| abstract_inverted_index.first | 81 |
| abstract_inverted_index.other | 97 |
| abstract_inverted_index.part, | 52 |
| abstract_inverted_index.rates | 132 |
| abstract_inverted_index.which | 47, 168, 193 |
| abstract_inverted_index.(IMel) | 45 |
| abstract_inverted_index.SAVEE, | 137 |
| abstract_inverted_index.become | 16 |
| abstract_inverted_index.common | 26 |
| abstract_inverted_index.higher | 159 |
| abstract_inverted_index.method | 27 |
| abstract_inverted_index.neural | 62 |
| abstract_inverted_index.number | 117 |
| abstract_inverted_index.obtain | 121 |
| abstract_inverted_index.people | 12 |
| abstract_inverted_index.proves | 169, 194 |
| abstract_inverted_index.reduce | 115 |
| abstract_inverted_index.single | 166 |
| abstract_inverted_index.smooth | 9 |
| abstract_inverted_index.sparse | 64 |
| abstract_inverted_index.speech | 30 |
| abstract_inverted_index.83.18%, | 145 |
| abstract_inverted_index.88.96%, | 143 |
| abstract_inverted_index.94.79%, | 142 |
| abstract_inverted_index.Because | 59 |
| abstract_inverted_index.EMO-DB, | 136 |
| abstract_inverted_index.RAVDESS | 139 |
| abstract_inverted_index.analyze | 57 |
| abstract_inverted_index.between | 11 |
| abstract_inverted_index.channel | 98 |
| abstract_inverted_index.emotion | 31 |
| abstract_inverted_index.extract | 68, 88 |
| abstract_inverted_index.focuses | 48 |
| abstract_inverted_index.further | 191 |
| abstract_inverted_index.highest | 130 |
| abstract_inverted_index.inverse | 43 |
| abstract_inverted_index.people. | 21 |
| abstract_inverted_index.pursued | 19 |
| abstract_inverted_index.results | 126 |
| abstract_inverted_index.speech. | 39 |
| abstract_inverted_index.CNN-SSAE | 199 |
| abstract_inverted_index.Mel-IMel | 73 |
| abstract_inverted_index.achieved | 133 |
| abstract_inverted_index.channel, | 82 |
| abstract_inverted_index.datasets | 140 |
| abstract_inverted_index.extracts | 99 |
| abstract_inverted_index.focusing | 33 |
| abstract_inverted_index.followed | 178 |
| abstract_inverted_index.machines | 14 |
| abstract_inverted_index.network. | 200 |
| abstract_inverted_index.proposed | 54 |
| abstract_inverted_index.contrast, | 41 |
| abstract_inverted_index.emotions. | 58 |
| abstract_inverted_index.features, | 71 |
| abstract_inverted_index.improved, | 192 |
| abstract_inverted_index.optimized | 70, 123, 184 |
| abstract_inverted_index.proposed. | 78 |
| abstract_inverted_index.structure | 76 |
| abstract_inverted_index.(CNN-SSAE) | 66 |
| abstract_inverted_index.artificial | 4 |
| abstract_inverted_index.background | 2 |
| abstract_inverted_index.autoencoder | 65 |
| abstract_inverted_index.conclusions | 148 |
| abstract_inverted_index.dimensions, | 119 |
| abstract_inverted_index.information | 91, 102, 108 |
| abstract_inverted_index.realization | 7 |
| abstract_inverted_index.recognition | 131, 152, 188 |
| abstract_inverted_index.transmitted | 110 |
| abstract_inverted_index.Experimental | 125 |
| abstract_inverted_index.dual-channel | 74 |
| abstract_inverted_index.information, | 185 |
| abstract_inverted_index.information. | 124 |
| abstract_inverted_index.recognition, | 32 |
| abstract_inverted_index.spectrogram, | 46 |
| abstract_inverted_index.spectrogram. | 95, 106 |
| abstract_inverted_index.spectrograms | 23, 157, 173 |
| abstract_inverted_index.communication | 10 |
| abstract_inverted_index.complementary | 75 |
| abstract_inverted_index.convolutional | 61 |
| abstract_inverted_index.effectiveness | 196 |
| abstract_inverted_index.intelligence, | 5 |
| abstract_inverted_index.low-frequency | 36, 90 |
| abstract_inverted_index.respectively. | 146 |
| abstract_inverted_index.spectrograms, | 167 |
| abstract_inverted_index.complementary. | 175 |
| abstract_inverted_index.high-frequency | 51, 101 |
| abstract_inverted_index.comprehensively | 56 |
| abstract_inverted_index.network-stacked | 63 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100445969 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I9086337 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.93358217 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |