## 1 Introduction
[PERSON] proposed a connectionist approach which contains a neural network of onset detection [22]. [PERSON] detected the onsets and estimated the pitches at the note attack using SVM [23]. However, little deep-learning-based research has been done in this category, to our knowledge. Modeling the instrument being transcribed and learning the corresponding timbral properties is an efficient way to improve the AMT performance. Instrument-specific transcription research restricts the employed instrument models to a specific type. Depending on the timbral properties of different instruments, different sets of constraints are adopted in instrument-specific AMT systems [24; 25; 26]. As a typical multi-pitch instrument, the piano has been widely studied in AMT because its polyphony is challenging. The task of piano transcription has existed in MIREX (Music Information Retrieval Evaluation eXchange) since 2007, and it is competitive every year [27]. Figure 1 gives MIREX's annual best results for the note tracking of piano subset based on onset only over the past 10 years. The current state-of-the-art system won 82% on F-measure in MIREX 2016, which is employed as a baseline system to evaluate the performance of our proposed method [28]. Individual-specific transcription is a new direction of AMT, which can make use of more characteristics of the individual piano. [PERSON] and [PERSON] modeled the temporal evolution of piano notes, and the spectrogram was factorized using the templates [29]. In the same context-dependent setting, they also employed convolutional sparse coding to transcribe the music from a specific piano in the specific environment [30]. In the supervised NMF, templates were usually formed by the isolated notes of the specific piano to be transcribed. [PERSON] employed spectro-temporal patterns to model the temporal evolution in NMF [31]. [PERSON] proposed a method to model the attack and decay of notes, and all the templates were trained by a Disklavier piano [32]. In the same transcription task, [PERSON] combined the convolutional NMF with a differential spectrogram [33]. In this paper, we focus on the note-based polyphonic transcription for a specific piano. Deep learning technique is adopted to recognize notes preliminarily, and then the candidate notes are verified for the specific piano. In the stage of note recognition, a convolutional neural network (CNN) is used to detect onsets, and another CNN is used to estimate the probabilities of pitches at each detected onset. During the note verification, the spectrogram is factorized using attack templates of notes. Compared to existing AMT approaches, the proposed method has the following advantages:  
1. The note recognition stage yields a note-level transcription by estimating the pitch at each onset. Compared to existing deep-learning-based methods which use a single network, two consecutive CNNs yield better performance. 2. An extra stage of note verification is conducted for the specific piano, in which the spectrogram factorization improves the precision of transcription. Compared with the traditional NMF, the proposed note verification stage could save computing time and storage space to a great extent. 3. The proposed method achieves better performance in specific piano transcription compared to the state-of-the-art approach. The outline of this paper is as follows. The proposed framework is described in Section 2. The transcription and comparison experiments are presented in Section 3. Finally, conclusions are drawn in Section 4.