39 research outputs found
μ‘°κ±΄λΆ μκΈ°νκ·ν μΈκ³΅μ κ²½λ§μ μ΄μ©ν μ μ΄ κ°λ₯ν κ°μ°½ μμ± ν©μ±
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μ΅ν©κ³ΌνκΈ°μ λνμ μ§λ₯μ 보μ΅ν©νκ³Ό, 2022. 8. μ΄κ΅κ΅¬.Singing voice synthesis aims at synthesizing a natural singing voice from given input information. A successful singing synthesis system is important not only because it can significantly reduce the cost of the music production process, but also because it helps to more easily and conveniently reflect the creator's intentions. However, there are three challenging problems in designing such a system - 1) It should be possible to independently control the various elements that make up the singing. 2) It must be possible to generate high-quality sound sources, 3) It is difficult to secure sufficient training data. To deal with this problem, we first paid attention to the source-filter theory, which is a representative speech production modeling technique. We tried to secure training data efficiency and controllability at the same time by modeling a singing voice as a convolution of the source, which is pitch information, and filter, which is the pronunciation information, and designing a structure that can model each independently. In addition, we used a conditional autoregressive model-based deep neural network to effectively model sequential data in a situation where conditional inputs such as pronunciation, pitch, and speaker are given. In order for the entire framework to generate a high-quality sound source with a distribution more similar to that of a real singing voice, the adversarial training technique was applied to the training process. Finally, we applied a self-supervised style modeling technique to model detailed unlabeled musical expressions. We confirmed that the proposed model can flexibly control various elements such as pronunciation, pitch, timbre, singing style, and musical expression, while synthesizing high-quality singing that is difficult to distinguish from ground truth singing. Furthermore, we proposed a generation and modification framework that considers the situation applied to the actual music production process, and confirmed that it is possible to apply it to expand the limits of the creator's imagination, such as new voice design and cross-generation.κ°μ°½ ν©μ±μ μ£Όμ΄μ§ μ
λ ₯ μ
보λ‘λΆν° μμ°μ€λ¬μ΄ κ°μ°½ μμ±μ ν©μ±ν΄λ΄λ κ²μ λͺ©νλ‘ νλ€. κ°μ°½ ν©μ± μμ€ν
μ μμ
μ μ λΉμ©μ ν¬κ² μ€μΌ μ μμ λΏλ§ μλλΌ μ°½μμμ μλλ₯Ό λ³΄λ€ μ½κ³ νΈλ¦¬νκ² λ°μν μ μλλ‘ λλλ€. νμ§λ§ μ΄λ¬ν μμ€ν
μ μ€κ³λ₯Ό μν΄μλ λ€μ μΈ κ°μ§μ λμ μ μΈ μꡬμ¬νμ΄ μ‘΄μ¬νλ€. 1) κ°μ°½μ μ΄λ£¨λ λ€μν μμλ₯Ό λ
립μ μΌλ‘ μ μ΄ν μ μμ΄μΌ νλ€. 2) λμ νμ§ μμ€ λ° μ¬μ©μ±μ λ¬μ±ν΄μΌ νλ€. 3) μΆ©λΆν νλ ¨ λ°μ΄ν°λ₯Ό ν보νκΈ° μ΄λ ΅λ€. μ΄λ¬ν λ¬Έμ μ λμνκΈ° μν΄ μ°λ¦¬λ λνμ μΈ μμ± μμ± λͺ¨λΈλ§ κΈ°λ²μΈ μμ€-νν° μ΄λ‘ μ μ£Όλͺ©νμλ€. κ°μ°½ μ νΈλ₯Ό μμ μ 보μ ν΄λΉνλ μμ€μ λ°μ μ 보μ ν΄λΉνλ νν°μ ν©μ±κ³±μΌλ‘ μ μνκ³ , μ΄λ₯Ό κ°κ° λ
립μ μΌλ‘ λͺ¨λΈλ§ν μ μλ ꡬ쑰λ₯Ό μ€κ³νμ¬ νλ ¨ λ°μ΄ν° ν¨μ¨μ±κ³Ό μ μ΄ κ°λ₯μ±μ λμμ ν보νκ³ μ νμλ€. λν μ°λ¦¬λ λ°μ, μμ , νμ λ± μ‘°κ±΄λΆ μ
λ ₯μ΄ μ£Όμ΄μ§ μν©μμ μκ³μ΄ λ°μ΄ν°λ₯Ό ν¨κ³Όμ μΌλ‘ λͺ¨λΈλ§νκΈ° μνμ¬ μ‘°κ±΄λΆ μκΈ°νκ· λͺ¨λΈ κΈ°λ°μ μ¬μΈ΅μ κ²½λ§μ νμ©νμλ€. λ§μ§λ§μΌλ‘ λ μ΄λΈλ§ λμ΄μμ§ μμ μμ
μ ννμ λͺ¨λΈλ§ν μ μλλ‘ μ°λ¦¬λ μκΈ°μ§λνμ΅ κΈ°λ°μ μ€νμΌ λͺ¨λΈλ§ κΈ°λ²μ μ μνλ€. μ°λ¦¬λ μ μν λͺ¨λΈμ΄ λ°μ, μμ , μμ, μ°½λ², νν λ± λ€μν μμλ₯Ό μ μ°νκ² μ μ΄νλ©΄μλ μ€μ κ°μ°½κ³Ό ꡬλΆμ΄ μ΄λ €μ΄ μμ€μ κ³ νμ§ κ°μ°½ ν©μ±μ΄ κ°λ₯ν¨μ νμΈνλ€. λμκ° μ€μ μμ
μ μ κ³Όμ μ κ³ λ €ν μμ± λ° μμ νλ μμν¬λ₯Ό μ μνμκ³ , μλ‘μ΄ λͺ©μ리 λμμΈ, κ΅μ°¨ μμ± λ± μ°½μμμ μμλ ₯κ³Ό νκ³λ₯Ό λν μ μλ μμ©μ΄ κ°λ₯ν¨μ νμΈνλ€.1 Introduction 1
1.1 Motivation 1
1.2 Problems in singing voice synthesis 4
1.3 Task of interest 8
1.3.1 Single-singer SVS 9
1.3.2 Multi-singer SVS 10
1.3.3 Expressive SVS 11
1.4 Contribution 11
2 Background 13
2.1 Singing voice 14
2.2 Source-filter theory 18
2.3 Autoregressive model 21
2.4 Related works 22
2.4.1 Speech synthesis 25
2.4.2 Singing voice synthesis 29
3 Adversarially Trained End-to-end Korean Singing Voice Synthesis System 31
3.1 Introduction 31
3.2 Related work 33
3.3 Proposed method 35
3.3.1 Input representation 35
3.3.2 Mel-synthesis network 36
3.3.3 Super-resolution network 38
3.4 Experiments 42
3.4.1 Dataset 42
3.4.2 Training 42
3.4.3 Evaluation 43
3.4.4 Analysis on generated spectrogram 46
3.5 Discussion 49
3.5.1 Limitations of input representation 49
3.5.2 Advantages of using super-resolution network 53
3.6 Conclusion 55
4 Disentangling Timbre and Singing Style with multi-singer Singing Synthesis System 57
4.1Introduction 57
4.2 Related works 59
4.2.1 Multi-singer SVS system 60
4.3 Proposed Method 60
4.3.1 Singer identity encoder 62
4.3.2 Disentangling timbre & singing style 64
4.4 Experiment 64
4.4.1 Dataset and preprocessing 64
4.4.2 Training & inference 65
4.4.3 Analysis on generated spectrogram 65
4.4.4 Listening test 66
4.4.5 Timbre & style classification test 68
4.5 Discussion 70
4.5.1 Query audio selection strategy for singer identity encoder 70
4.5.2 Few-shot adaptation 72
4.6 Conclusion 74
5 Expressive Singing Synthesis Using Local Style Token and Dual-path Pitch Encoder 77
5.1 Introduction 77
5.2 Related work 79
5.3 Proposed method 80
5.3.1 Local style token module 80
5.3.2 Dual-path pitch encoder 85
5.3.3 Bandwidth extension vocoder 85
5.4 Experiment 86
5.4.1 Dataset 86
5.4.2 Training 86
5.4.3 Qualitative evaluation 87
5.4.4 Dual-path reconstruction analysis 89
5.4.5 Qualitative analysis 90
5.5 Discussion 93
5.5.1 Difference between midi pitch and f0 93
5.5.2 Considerations for use in the actual music production process 94
5.6 Conclusion 95
6 Conclusion 97
6.1 Thesis summary 97
6.2 Limitations and future work 99
6.2.1 Improvements to a faster and robust system 99
6.2.2 Explainable and intuitive controllability 101
6.2.3 Extensions to common speech synthesis tools 103
6.2.4 Towards a collaborative and creative tool 104λ°
무μ λ―Έ EMS λμ°λ³μ΄μ²΄μ μ μ μ λΆμ
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : λμ
μλͺ
κ³Όνλν μλ¬Όμμ°κ³ΌνλΆ, 2018. 2. κ°λ³μ² .Capsaicinoid is the alkaloid compounds produced in peppers
(Capsicum spp.). They are responsible for pepper pungency or hotness
and is one of the important traits in breeding programs. Although many
studies have been performed to elucidate its biosynthesis, the
biosynthetic pathway is largely based on studies on the similar pathways
of other plants. To understand the biosynthesis of capsaicinoid, a nonpungent
mutant 221-2-1a, developed from pungent 'Yuwol-cho' were
analyzed. 221-2-1a was found to have no mutation in the coding
sequence of Pun1, but the levels of capsaicinoid in their fruits were
drastically decreased compared to that of Yuwol-cho. To identify the
gene(s) responsible for the non-pungent trait in 221-2-1a. Gene
expressions of 12 genes involved in capsaicinoid biosynthesis were
compared between 221-2-1a and Yuwol-cho together with several
selected cultivars. Seven out of 12 genes (pAMT, BCAT, ACL, KAS, FatA,
PAL, and Pun1) showed a significant decrease in their expression levels
in 221-2-1a compared to pungent cultivars. Furthermore, the inheritance
of pungency was studied in a population derived from a between Yuwolcho
and 221-2-1a. The inheritance study showed that the nonpungency
in 221-2-1a is controlled by two recessive genes. To identify the genes
responsible for non-pungency trait, samples of Yuwol-cho and bulked F3
were sequenced and analyzed by MutMap. A total of 11 SNPs were
identified in the intergenic sequences and the candidates were annotated.
Although candidate genes were not capsaicinoid biosynthesic genes, the
candidate genes are believed to be ideal targets in studies to carryout in
the future.I. LITERATURE REVIEW 01
II. INTRODUCTION 07
III. MATERIALS AND METHODS 10
3.1 Plant materials 10
3.2 Sample preparation for capsaicinoid analysis.. 10
3.3 Gibbs screening and HPLC analysis . 11
3.4 Isolation of RNA and cDNA synthesis 13
3.5 Quantitative real-time PCR analysis. 13
3.6 Polymerase chain reaction (PCR) amplification and sequence analysis of Pun1. 17
3.7 Genomic DNA extraction 17
3.8 Whole genome sequencing of wild type and mutant bulk 18
3.9 Alignment of reference sequence and MutMap.. 18
IV. RESULTS 20
4.1 Capsaicinoid measurement in placenta tissues . 20
4.2 Expression analysis of capsaicinoid pathway genes. 23
4.3 Comparison of Pun1 exon sequences . 28
4.4 Evaluation of pungency segregation. 30
4.5 Sequencing of Yuwol-cho and bulked F3 DNA 34
4.6 MutMap analysis for candidate region identification 36
V. DISCUSSION 40
VI. REFERENCES. 44
VII. ABSTRACT IN KOREAN 55
VIII. APPENDIX. 57Maste
A Numerical Simulation of Near-Cloud Turbulence Associated with Tropical Cyclone Hagibis
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν νλκ³Όμ κ³μ°κ³Όνμ 곡, 2023. 2. κΉμ ν.2019λ
10μ 11μΌ 0840μμ 0900 UTC κΈ°κ°μ λΆμ ννμ μ곡μ μ§λλ νκ³΅κΈ°κ° νν νκΈ°λΉμ€ λΆμμͺ½ μ λ©΄μ μ½ 11 km κ³ λμμ λ€λ°μ±μ λλ₯λ₯Ό μ‘°μ°νμλ€. λλ₯ λ°μ μ§μ μ νν μ€μ¬μΌλ‘λΆν° 500 km μ΄μ λ¨μ΄μ Έ μμμΌλ©°, ν곡기 κ΄μΈ‘ μλ£μ λ°λ₯΄λ©΄ μλμ§ μμ°λ₯ μ΄ 0.22 m2/3s-1 μ΄μμΈ μ€κ°λ ν¬κΈ°λ₯Ό κ°λ λλ₯λ₯Ό ν¬ν¨νμλ€. λ°λΌμ λ³Έ μ°κ΅¬μμλ λ³Έ λλ₯ μ¬λ‘μ λ°μ λ©μ»€λμ¦μ μ΄ν΄λ³΄κΈ° μν΄ Weather and Forecasting (WRF)μ μ΄μ©ν μμΉλͺ¨λΈ μ€νμ μννμλ€. μμΉμ€νμ λλ₯ κ΄μΈ‘ μ§μ μ μ€μ¬μΌλ‘ μν격μκ° κ°κ° 15, 5, 1, 0.2 kmμΈ μμ 4κ°λ‘ ꡬμ±νμμΌλ©°, μ°μ§ ν΄μλλ λλ₯ λ°μ ꡬκ°μΈ 8-13 kmμμ μ½ 280 mλ‘ μ€μ νμλ€. μμ λκΈ°μμμ μ°μ§ νΌν© λͺ¨μνλ₯Ό μν΄ Mellor-Yamada 2.5μ°¨ λλ₯ μ’
κ²° λ°©λ²λ‘ μ μ΄μ©νμ¬ μ격μ κ·λͺ¨μ λλ₯ μ΄λμλμ§(subgrid-scale turbulent kinetic energy, μ΄ν SGS TKE)λ₯Ό μ°μΆνλ Mellor-Yamada-JanjiΔ λ°©μμ κ° μμμ μ μ©νμλ€. κ·Έ κ²°κ³Ό SGS TKEκ° 0.25 m2s-2 μ΄μμΈ κ°ν λλ₯κ° κ΅μ§μ μΌλ‘ μΈ κ΅¬κ° 1) z = 13-15 km, 2) z = 10-12 km, 3) z = 6-8 kmμμ λͺ¨μλμμΌλ©°, λ³Έ μ°κ΅¬μμλ μ€κ°λμ λλ₯κ° κ΄μΈ‘λ 2) ꡬκ°μ μ€μ μ μΌλ‘ λΆμνμλ€. λλ₯ λ°μ 2μκ° μ΄μ λΆν° λͺ¨λ£¨μ΄ νμΈ΅μ μμΉν 2) ꡬκ°μμλ νν μμΈ΅μ κ³ κΈ°μμ± μ μΆλ‘ μ λλ κ°ν μ°μ§ μμ΄μ μν Kelvin-Helmholtz λΆμμ μ΄ μ§μμ μΌλ‘ μ‘΄μ¬νμλ€. μ΄ν 0800 UTC λΆν° μ μ μμ λκ° κ°μνμ¬ 0840-0900 UTCμλ 2) κ΅¬κ° λλ₯ λ°μμ μ§μ μ μμΈμΈ λλ₯ λΆμμ μ΄ λ°μνμλ€. μ΄λ νν μμΈ΅μ κ³ κΈ°μ νλ¦μ μν μ€λ¦½ νΉμ μ½ν κ΄μ± λΆμμ μ΄ μ§μμ μΌλ‘ μ‘΄μ¬νλ 2) ꡬκ°μμλ λλ₯ κ΄μΈ‘ μκΈ°μ ννμ λΆμμΌλ‘ κ³ κΈ°μμ± νλ¦μ΄ κ°νλμ΄ κ΄μ± λΆμμ λκ° μ¦κ°νμλ€. μ¦ κ΄μ± λΆμμ κ°νμκΈ°μ λλ₯ λΆμμ μ λ°ν μκΈ°κ° μΌμΉνμμΌλ©°, μ΄λ₯Ό ν΅ν΄ κ΄μ± λΆμμ μ΄ 2) κ΅¬κ° λλ₯ λ°μμ μ£ΌμμΈμΈ λλ₯ λΆμμ λ°νμ μ€μ μμλ‘ μμ©ν κ²μΌλ‘ ν΄μλλ€. ννΈ, 1) ꡬκ°μ λλ₯ λ°μμ νν κ³ κΈ°μμ± νλ¦ λ΄ μ¨λ μ΄λ₯ μ°¨μ΄μ μν μμ λλ₯ λΆμμ , 3) ꡬκ°μ λλ₯ λ°μμ κΆμ΄ λ΄ λ¨μ΄μ§λ λ μ
μμ μΉν κ³Όμ μΌλ‘ λ°μνλ λλ₯ λΆμμ λλ¬ΈμΈ κ²μΌλ‘ λΆμλλ€.From 0840 to 0900 UTC 11 October 2019, light-or-moderate turbulence events were observed with in situ eddy dissipation rate data provided by Aircraft Meteorological Data Relay at 11 km within the anticyclonic outflow of tropical cyclone (TC) Hagibis over the northwestern Pacific Ocean. The area of turbulence was farther than 500 km from the central of the TC and showed the low density of cloud. The generation mechanism of near-cloud turbulence (NCT) occurred in the northwestern side of the TC was examined using the Weather Research and Forecasting (WRF) model. Four nested model domains with horizontal grid spacings of 15, 5, 1, and 0.2 km and 112 hybrid layers with vertical grid spacing of about 280 m within z = 8-13 km, near altitudes where the NCT encounters occurred were used. The Mellor-Yamada-Janjic scheme was applied in each domain to parameterize local vertical mixings by computing subgrid-scale turbulent kinetic energy (SGS TKE) in free atmosphere by Mellor-Yamada 2.5-level turbulence closure method. The results showed that there were three distinct areas of simulated turbulence, which occurred in 1) z = 13-15 km, 2) z = 10-12 km, and 3) z = 6-8 km layers showing SGS TKE larger than 0.25 m2s-2. We focused on the 10 to 12 km layer in which turbulence was observed. Richardson (Ri) number smaller than 0.25 was found consistently before the time of the incident, which implies Kelvin-Helmholtz instability occurred due to strong vertical wind shear induced by anticyclonic outflow of the TC at the beneath of the cirrus anvil cloud. From 0800 UTC, static stability started to decrease and convective instability occurred during 0840 to 0900 UTC, which produced light-or-moderate level turbulence. At the same time, intensity of inertial instability at z = 10-12 km layer increased with strengthened upper-level anticyclonic outflow where neutral or weak inertial instability was consistently existed due to anticyclonic outflow of the TC. Consequently, we suggested that inertial instability was responsible for the occurrence of convective instability given that the strengthening period of inertial instability was coincided with the manifestation period of convective instability. SGS TKE simulated at z = 13-15 km was due to convective instability induced by the differential thermal advection within the anticyclonic outflow of the TC. SGS TKE found at z = 6-8 km was also induced by convective instability by sublimation of precipitating snow in the beneath of cirrus anvil cloud.1. μλ‘ 1
2. μ¬λ‘ μ μ 6
3. μ€ν μ€κ³ 13
4. μ€ν κ²°κ³Ό κ°μ 17
5. λλ₯ λ©μ»€λμ¦ λΆμ 24
5.1 ν‘μ κΆμ΄ λ°΄λ λ΄ λλ₯ λ°μ 24
5.2 νν μμΈ΅ κ³ κΈ°μμ± νλ¦ λ΄ λλ₯ λ°μ 40
5.3 λͺ¨λ£¨μ΄ νλ¨ λ΄ λλ₯ λ°μ 61
6. μμ½ λ° κ²°λ‘ 66
μ°Έκ³ λ¬Έν 71
Abstract 78μ
μ€νλ‘λ©μ μ΄μ©ν λͺ¨λ°μΌ κΈ°κΈ°μμμ μ€μκ° μ΄λ―Έμ§ μ΄ν΄μλ κΈ°μ
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2018. 8. μ΅μ±ν.The rapid enhancement of camera performances in smartphones has allowed users to take high quality pictures without high-end digital cameras. However, there still remains a large gap between smartphone cameras and digital cameras when in comes to zoom-in functionality. Most smartphones provide only digial zoom-in functionality, where image quality degradation is inevitable when the user enlarges the image. Even the high-end smartphones embedded with optical lens provide limited optical zoom-in capabilities, leaving users with great inconvenience. While users can employ an external optical lens to utilize the optical zoom-in functionality, having to carry around an extra hardware incurs great overhead, not to mention its price.
Image Super Resolution (SR) can be a solution to overcome this limitation by recovering the quality degradation caused by digital zoom-in. Image SR, a technique to restore high frequency details from a Low Resolution (LR) image to obtain a High Resolution (HR) image, has been a traditional field of research in computer vision. As deep learning based, especially Convolutional Neural Network (CNN) based, methods have shown to outperform traditional methods, and have been actively researched in recent years.
In this paper, we exploit deep learning based image SR to replace the optical zoom-in functionality in smartphones without embedded optical lenses. As there are several resource constraints in smartphones~(e.g., computing power, energy, memory), challenges occur when aiming to provide a real-time performance relying solely based on local execution. To tackle the challenge, we propose a server offloading based approach to provide higher frame rate. Through a prototype implementation on Android and extensive experiments in real world environments, we show that our proposed system can provide at least 10~fps.1 Introduction 1
2 Preliminaries 4
2.1 Image Super Resolution (SR) . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Mobile deep learning framework . . . . . . . . . . . . . . . . . . . . 5
2.2.1 Local execution based framework . . . . . . . . . . . . . . . 5
2.2.2 Server offloading based framework . . . . . . . . . . . . . . 6
2.3 What is different about SR in smartphones . . . . . . . . . . . . . . 6
2.3.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3.2 Resource constraints . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Local or server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Implementation 8
3.1 SR model implementation . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Prototype implementation on Android . . . . . . . . . . . . . . . . . 9
4 Evaluation 11
4.1 SR model performance . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.2 Inference time measured on smartphone and server . . . . . . . . . . 12
4.3 Latency analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.3.1 Offloading latency . . . . . . . . . . . . . . . . . . . . . . . 14
4.3.2 Overall latency breakdown . . . . . . . . . . . . . . . . . . . 17
5 Discussion 19
5.1 Perceptual quality of generated images . . . . . . . . . . . . . . . . . 19
5.2 Managing high data rate . . . . . . . . . . . . . . . . . . . . . . . . 20
6 Conclusion 22
Abstract (In Korean) 28
κ°μ¬μκΈ 31Maste
Mobile telemedicine system via PSTN
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[μλ¬Έ]
There is a need for caring the elderly and disabled people without increasing the cost of on-site expertise at the location where home health nursing services are provided.
Recently, telemedicine is changing the traditional form of health care delivery, by providing cost-effective technical solutions to communicate between patients and doctor. Despite of high reliability, ISDN-based telemedicine systems are not widely
used in home health care because of high communication cost.
In this study, an efficient and inexpensive electronic system to transmit audio and video signal in telemedicine via PSTN was presented. The Mobile Telemedicine System via PSTN was developed and implemented to provide health care for elderly and disabled people at Kwachon as a demonstration project. The telemedicine systems consisted of notebook computer and digital camera interconnected via PSTN, which enables communication between the patient at home and the doctor at health center.
After eight months trial of this project, 50 patients are being cared for at the patient's home or the nursing home for the old. The ratio of satisfied patients was 76% and the number of patient's visit to the clinic was reduced. The quality of video and audio did not have an influence on satisfaction. The results suggested that telemedicine was both feasible and acceptable via PSTNrestrictio
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : κ²½μνκ³Ό, 2015. 2. μ΄κ΄ν.I examine the claim argued by Novy-Marx (2012) that the intermediate past performance of stock does better job in explaining conventional momentum profit. Using equity level data from 49 countries, I find that there exists a cross-country variation in the superiority of performance of momentum portfolio based on intermediate past return, and such variation can be explained by the individualism index established by Hofstede (2001). Partly confirming the finding by Chui, Titman, and Wei (2010), I argue that the finding by Novy-Marx is a manifestation of cross-country cultural differences that have direct impact on investor behavior, expressed in terms of momentum profit.1. Introduction 1
2. Data Selection and Methodology 4
3. Term Structure of Momentum around the world 5
4. Behavioral explanation β Individualism index 9
5. Conclusion 14
6. Reference 16
7. Tables and Figures 19
8. Appendix 29
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