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品牌推广工作内容,百度seo技术优化,公司网站域名是什么,wordpress 速度变慢算法基本流程如下#xff1a;

1. 采集音乐库 2. 音乐指纹采集 3. 采用局部最大值作为特征点 4. 将临近的特征点进行组合形成特征点对 5. 对每个特征点对进行hash编码 编码过程#xff1a;将f1和f2进行10bit量化#xff0c;其余bit用来存储时间偏移合集形成32bit的hash码 … 算法基本流程如下 

2. 采集音乐库 2. 音乐指纹采集 3. 采用局部最大值作为特征点 4. 将临近的特征点进行组合形成特征点对 5. 对每个特征点对进行hash编码 编码过程将f1和f2进行10bit量化其余bit用来存储时间偏移合集形成32bit的hash码 Hash f1|f210|diff_t20存储信息t1Hash 实现 import numpy as np import librosa from scipy import signal import pickle import os fix_rate 16000 win_length_seconds 0.5 frequency_bits 10 num_peaks 15# 构造歌曲名与歌曲id之间的映射字典 def song_collect(base_path):index 0dic_idx2song {}for roots, dirs, files in os.walk(base_path):for file in files:if file.endswith((.mp3, .wav)):file_song os.path.join(roots, file)dic_idx2song[index] file_songindex 1return dic_idx2song# 提取局部最大特征点 def collect_map(y, fs, win_length_seconds0.5, num_peaks15):win_length int(win_length_seconds * fs)hop_length int(win_length // 2)S librosa.stft(y, n_fftwin_length, win_lengthwin_length, hop_lengthhop_length)S np.abs(S) # 获取频谱图D, T np.shape(S)constellation_map [] for i in range(T):spectrum S[:, i]peaks_index, props signal.find_peaks(spectrum, prominence0, distance200)# 根据显著性进行排序n_peaks min(num_peaks, len(peaks_index))largest_peaks_index np.argpartition(props[prominences], -n_peaks)[-n_peaks:]for peak_index in peaks_index[largest_peaks_index]:frequency fs / win_length * peak_index# 保存局部最大值点的时-频信息constellation_map.append([i, frequency])return constellation_map# 进行Hash编码 def create_hash(constellation_map, fs, frequency_bits10, song_idNone):upper_frequency fs / 2hashes {}for idx, (time, freq) in enumerate(constellation_map):for other_time, other_freq in constellation_map[idx: idx 100]: # 从邻近的100个点中找点对diff int(other_time - time)if diff 1 or diff 10: # 在一定时间范围内找点对continuefreq_binned int(freq / upper_frequency * (2 ** frequency_bits))other_freq_binned int(other_freq / upper_frequency * (2 ** frequency_bits))hash int(freq_binned) | (int(other_freq) 10) | (int(diff) 20)hasheshashreturn hashes 特征提取feature_collect.py

   获取数据库中所有音乐

   path_music data current_path os.getcwd() path_songs os.path.join(current_path, path_music) dic_idx2song song_collect(path_songs)# 对每条音乐进行特征提取 database {} for song_id in dic_idx2song.keys():file dic_idx2song[song_id]print(collect info of file, file)# 读取音乐y, fs librosa.load(file, srfix_rate)# 提取特征对constellation_map collect_map(y, fs, win_length_secondswin_length_seconds, num_peaksnum_peaks)# 获取hash值hashes create_hash(constellation_map, fs, frequency_bitsfrequency_bits, song_idsong_id)# 把hash信息填充入数据库for hash, time_index_pair in hashes.items():if hash not in database:database[hash] []database[hash].append(time_index_pair)# 对数据进行保存 with open(database.pickle, wb) as db:pickle.dump(database, db, pickle.HIGHEST_PROTOCOL) with open(song_index.pickle, wb) as songs:pickle.dump(dic_idx2song, songs, pickle.HIGHEST_PROTOCOL)

   加载数据库

   database pickle.load(open(database.pickle, rb)) dic_idx2song pickle.load(open(song_index.pickle, rb)) print(len(database))# 检索过程 def getscores(y, fs, database):# 对检索语音提取hashconstellation_map collect_map(y, fs)hashes create_hash(constellation_map, fs, frequency_bits10, song_idNone)# 获取与数据库中每首歌的hash匹配matches_per_song {}for hash, (sample_time, _) in hashes.items():if hash in database:maching_occurences database[hash]for source_time, song_index in maching_occurences:if song_index not in matches_per_song:matches_per_song[song_index] []matches_per_song[song_index].append((hash, sample_time, source_time))scores {}# 对于匹配的hash计算测试样本时间和数据库中样本时间的偏差for song_index, matches in matches_per_song.items():

   scores[song_index] len(matches)song_scores_by_offset {}# 对相同的时间偏差进行累计for hash, sample_time, source_time in matches:delta source_time - sample_timeif delta not in song_scores_by_offset:song_scores_by_offset[delta] 0song_scores_by_offset[delta] 1# 计算每条歌曲的最大累计偏差max (0, 0)for offset, score in song_scores_by_offset.items():if score max[1]:max (offset, score)scores[song_index] maxscores sorted(scores.items(), keylambda x:x[1][1], reverseTrue)return scores

   音乐检索music_research.py import threading from playsound import playsounddef cycle(path):while 1:playsound(path) def play(path, cycFalse):if cyc:cycle(path)else:playsound(path)path test_music/record4.wav y, fs librosa.load(path, srfix_rate)

   播放待检索音频

   music threading.Thread(targetplay, args(path,)) music.start()# 检索打分 scores getscores(y, fs, database)# 打印检索信息 for k, v in scores:file dic_idx2song[k]name os.path.split(file)[-1]# print(%s :%d%(name, v))print(%s: %d: %d%(name, v[0], v[1]))# 打印结果 if len(scores) 0 and scores[0][1][1] 50:print(检索结果为:, os.path.split(dic_idx2song[scores[0][0]])[-1]) else:print(没有搜索到该音乐) 麦克风录音识别音乐 import pyaudio import waveRATE 48000 # 采样率 CHUNK 1024 # 帧大小 record_seconds 10 # 录音时长s CHANNWLS 2 # 通道数# 创建pyaudio流 audio pyaudio.PyAudio()stream audio.open(formatpyaudio.paInt16, # 使用量化位数16位channelsCHANNWLS, # 输入声道数目rateRATE, # 采样率inputTrue, # 打开输入流frames_per_bufferCHUNK) # 缓冲区大小frames [] # 存放录制的数据

   开始录音

   print(录音中。。。) for i in range(0, int(RATE / CHUNK * record_seconds)):# 从麦克风读取数据流data stream.read(CHUNK)# 将数据追加到列表中frames.append(data)# 停止录音关闭输入流 stream.stop_stream() stream.close() audio.terminate()# 将录音数据写入wav文件中 with wave.open(test_music/test.wav, wb) as wf:wf.setnchannels(CHANNWLS)wf.setsampwidth(audio.get_sample_size(pyaudio.paInt16))wf.setframerate(RATE)wf.writeframes(b.join(frames))# 打开录音文件 path test_music/test.wav y, fs librosa.load(path, srfix_rate)# 线程播放待检索音频

   music threading.Thread(targetplay, args(path,))

   music.start()# 音乐检索

   print(检索中。。。) scores getscores(y, fix_rate, database)# 打印检索信息

   for k, v in scores:

   file dic_idx2song[k]

   name os.path.split(file)[-1]

   # print(%s :%d%(name, v))

   print(%s: %d: %d%(name, v[0], v[1]))# 打印结果

   if len(scores) 0 and scores[0][1][1] 50:print(检索结果为:, os.path.split(dic_idx2song[scores[0][0]])[-1]) else:print(没有搜索到该音乐) 参考音乐检索-Shazam算法原理_哔哩哔哩_bilibili